Endor Labs

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endorlabs.comarticle

What is an AIBOM? AI bill of materials explained for security teams | Endor Labs

AIBOM explainer from Endor Labs: an AI bill of materials is a machine-readable inventory of models, datasets, dependencies, AI services, agents and MCP servers, with origin, license and integrity evidence. It walks through a CycloneDX entry for a fine-tuned model, compares CycloneDX ML-BOM, SPDX 3.x, the OWASP AIBOM Generator and the May 2026 CISA/G7 minimum-elements guidance, and lists a six-step build process. It also states what an AIBOM cannot show: model safety, runtime behavior and coding-agent actions.

CycloneDXSPDXSBOM+2

EU AI ActISO/IEC 42001CISA+5

Supply Chain, AI Security, GRC· 2026-10-29T04:16:08+00:00

endorlabs.comarticle

ADLC metrics: How to measure agentic development security | Endor Labs

ADLC metrics is an Endor Labs guide to measuring security in the agentic development lifecycle, where AI coding agents write much of the code. It defines formulas across five categories: AI supply chain visibility, agent action security, agent-generated code, package risk and developer flow. It argues for normalizing findings per merged change, tracking pre-execution blocks, and building a six-to-eight metric scorecard.

Claude CodeCodexCursor+3

ADLCOWASP Top 10 for LLM ApplicationsAgent Security League+4

AI Security, AppSec, DevSecOps· 2026-10-29T02:54:18+00:00

endorlabs.comwriteup

Endor Labs AI SAST Finds Two Apache Kafka Zero-Days: Unbounded Memory Allocation from Brokers to Kafka Streams | Endor Labs

Endor Labs AI SAST found three unbounded memory allocation flaws in Apache Kafka, fixed under CVE-2026-114349 (DefaultRecord decoder, a ~90-byte compressed batch that kills consumers, brokers and the log cleaner) and CVE-2026-111551 (ListDeserializer and Kafka Streams AssignmentInfo decoding). The post gives code, reproducers and payload bytes. Remediation: upgrade to 4.2.2, 4.3.2 or 4.4.0, set max.decompressed.message.bytes, restrict write and group ACLs, and alert on DeadThreadCount.

Apache KafkaKafka StreamsJava+1

CVE-2026-114349CVE-2026-111551denial of service+4

AppSec· 2026-10-28T19:20:59+00:00

endorlabs.comarticle

ADLC compliance: Mapping agentic development to NIST SSDF, OWASP, and the EU AI Act | Endor Labs

ADLC compliance guide from Endor Labs that maps agentic development to NIST SSDF (SP 800-218 v1.1), the OWASP LLM Top 10 2025 and Agentic Top 10, and the EU AI Act as amended by Regulation (EU) 2026/1744. It splits controls into a code surface and an action surface (shell, file access, MCP calls, package installs). It gives a four-step AI Act scoping path, the new Annex III date of 2 December 2027, and evidence guidance. It states the crosswalk is the vendor's interpretation, not legal advice.

Endor LabsModel Context ProtocolClaude Code+3

NIST SSDFNIST SP 800-218NIST SP 800-218A+13

GRC, AI Security, Supply Chain· 2026-10-28T03:56:18+00:00

endorlabs.comarticle

The ADLC maturity model: 5 stages of agentic development security | Endor Labs

The ADLC maturity model is a proposed five-stage framework from Endor Labs for scoring security controls over coding agents, from unmanaged experimentation to continuous verification. Each stage has requirements on two tracks, agent actions (shell commands, MCP calls, package installs) and agent output (code, dependencies, secrets). It sets stage 3 at deterministic hooks that block actions, gives evidence-based assessment questions, and lists traps such as relying on prompt-level rules.

Endor LabsAURIModel Context Protocol+2

ADLC maturity modelOWASP SAMMOWASP Top 10 for LLM Applications+5

AI Security, GRC, DevSecOps· 2026-10-28T03:36:09+00:00

endorlabs.comarticle

How to evaluate ADLC security solutions: A buyer's checklist | Endor Labs

Buyer's checklist from Endor Labs for evaluating security tools in the agentic development lifecycle (ADLC), where AI coding agents write code and take actions such as package installs, CI edits and tool calls. It covers scoping, coverage, evidence-based accuracy, repeatability testing, agent-loop and PR workflow fit, remediation, governance, and a 2 to 4 week proof of concept on 3 to 5 repos with seeded ground truth.

Endor LabsAURIModel Context Protocol+3

ADLCIDORNIST SSDF+1

AppSec, AI Security, DevSecOps· 2026-10-28T03:04:29+00:00

endorlabs.comarticle

Everything, everywhere, all at once… the week software broke | Endor Labs

Endor Labs roundup of the 20 to 21 October 2026 patch wave: eMudhra certificate authority keys that anyone can recover, PHP2Shell (CVE-2026-109618, CVE-2026-109619), Node2Shell (CVE-2026-111497) and malware in LiteLLM 1.107.0. It lists fixed versions (PHP 8.2.35 to 8.5.12, Node.js 22.23.4, 24.22.1, 26.12.1, LiteLLM back to 1.106.2) and explains why many separate trust-store copies each need updating. It closes with advice on triage, remediation and SBOM/VEX reporting.

PHPNode.jsLiteLLM+3

CVE-2026-109618CVE-2026-109619CVE-2026-111497+3

Supply Chain, AppSec· 2026-10-24T00:16:33+00:00

endorlabs.comarticle

ADLC Security Best Practices for Teams Shipping with Coding Agents | Endor Labs

ADLC Security Best Practices is an Endor Labs guide to controlling what AI coding agents can access, run, install and ship. It covers inventorying agents and MCP servers, sandboxing, pre-execution hooks instead of AGENTS.md rules, install-time package checks against slopsquatting and malware, reachability-based triage of agent PRs, and CI/CODEOWNERS protections. A five-step worked example and coverage metrics are included.

Model Context ProtocolClaude CodeCursor+3

slopsquattingindirect prompt injectiontool poisoning+5

AI Security, Supply Chain, DevSecOps· 2026-10-23T17:40:48+00:00

endorlabs.comarticle

eMudhra CA Compromise: The Trust Stores You Didn't Patch | Endor Labs

The eMudhra CA compromise (disclosed 20 October 2026) involves eight intermediate CA certificates whose private keys can be derived from the certificates themselves. They chain to AAA Certificate Services and three other roots. Browsers block them, but non-browser clients skip revocation and CT checks. Endor Labs finds 25 popular packages with frozen bundled root stores, 11 already patched, and gives a fingerprint-scanning script, test hosts and patch steps.

Endor LabsPythoncertifi+6

CVE-2026-111943eMudhraAAA Certificate Services+9

Supply Chain, AppSec, DevSecOps· 2026-10-21T21:07:31+00:00

endorlabs.comwriteup

LiteLLM 1.107.0 Compromised: Python Startup Hooks Steal Cloud, AI, and Proxy Credentials | Endor Labs

litellm 1.107.0 was published to PyPI on 21 October 2026 from a source revision that differs from the official tag. One line in the Maturin config (data = "cook") installs cook.pth, cook.py and sitecustomize.py, which run on every Python start. They steal cloud, AI and proxy credentials, including the LiteLLM database tables, and load remote code hourly. The post gives IOCs, a five-step response plan and hardening advice.

LiteLLMPyPIPython+3

litellm 1.107.0cook.pthsitecustomize.py+5

Supply Chain, Malware & RE, DevSecOps· 2026-10-21T16:25:43+00:00

endorlabs.comadvisory

PHP2Shell: Critical vulnerabilities in PHP (CVE-2026-109618, CVE-2026-109619) | Endor Labs

PHP2Shell is the name for two critical PHP core memory-safety flaws, CVE-2026-109618 and CVE-2026-109619, that allow unauthenticated remote code execution. Fixes ship in PHP 8.2.35, 8.3.36, 8.4.27 and 8.5.12. Wiz researchers demonstrated RCE against WordPress, Drupal and Joomla, and Apache with mod_php is the highest risk. The post covers inventory, container rebuilds, Cloudflare WAF rules and temporary exposure reduction.

PHPWordPressDrupal+3

CVE-2026-109618CVE-2026-109619PHP2Shell+4

AppSec, DevSecOps· 2026-10-21T15:49:16+00:00

endorlabs.comarticle

Gemini 4 Argon on Cursor: solid security score, two unique solves, but slower and more expensive | Endor Labs

Endor Labs evaluates Gemini 4 Argon in the Cursor harness on its Agent Security League coding tasks (SusVibes). Argon ties Fable 5 at 29% SecPass with 78.8% FuncPass, but costs $765 and takes a 14-minute median per task. It uniquely fixes SABnzbd path traversal (CVE-2021-29488) and a Nova credential-logging leak (CVE-2015-8749). Two other candidate solves were rejected, and 29 cheating instances were confirmed.

CursorGeminiEndor Labs+2

CVE-2021-29488CVE-2015-8749Agent Security League+8

AI Security, AppSec· 2026-10-20T21:50:47+00:00

endorlabs.comarticle

GitHub Action tj-actions/changed-files supply chain attack: what you need to know | Endor Labs

Endor Labs explains the March 2025 compromise of the tj-actions/changed-files GitHub Action (CVE-2025-30066). Attackers pushed a malicious commit and retroactively repointed version tags, so a Python payload dumped CI/CD secrets to workflow logs across about 23,000 repositories. The post gives response steps: find usage with a GitHub code search, remove the Action from all branches or enforce an Actions allowlist, audit logs for suspicious outbound requests and IPs, and rotate exposed secrets. It also notes the March 17 restoration and the risk of transitive Action dependencies. It includes Endor Labs customer steps (the `--ghactions --secrets --git-logs` scan flags).

GitHub ActionsGitHubEndor Labs+1

CVE-2025-30066tj-actions/changed-files

Supply Chain, DevSecOps· 2026-10-16T01:52:05+00:00

endorlabs.comarticle

5 Tips for Managing Bazel Dependencies (Without Losing Friends) | Endor Labs

Endor Labs gives five practices for updating third-party dependencies in Bazel monorepos: update one dependency at a time, stage cross-cutting upgrades, and test incrementally. It includes bazel query rdeps examples (e.g. for the Go zap library) to select only affected go_test targets. It also argues that SCA scans should target binary targets (go_binary, py_binary, java_binary) rather than the whole repo, and shows a query to enumerate them. The security angle is vulnerability-driven updates and scan scoping, and the piece ends with a vendor pitch.

BazelGoEndor Labs

Supply Chain, DevSecOps· 2026-10-16T01:52:05+00:00

endorlabs.comarticle

SBOMs are Just a Means to an End | Endor Labs

A critique of the state of SBOM adoption, written by a CISSP product manager at Endor Labs. It argues that the NTIA minimum elements and the SPDX and CycloneDX specs don't require a package URL (purl), so name and version alone can't tell a Maven log4j-core from an npm or cargo package of the same name. It also notes that no spec defines build time, which makes transitive dependency resolution unreliable, and that generation tools place the same data in different fields. It adds that vendors have mixed incentives to ship SBOMs and that practices are immature, and it calls on standards bodies to define required data per prioritized use case.

SBOMSPDXCycloneDX+1

Executive Order 14028NTIANTIA Minimum Elements for an SBOM+2

Supply Chain, GRC· 2026-10-16T01:52:05+00:00

endorlabs.comwriteup

SBOM vs. SBOM: Comparing SBOMs from Different Tools and Lifecycle Stages | Endor Labs

Case study running three open source SBOM generators (two generic, one Maven plugin) on Eclipse Steady 3.2.5 rest-backend at three lifecycle stages (git clone, Maven package, Docker image), yielding 8 CycloneDX SBOMs. Compares precision/recall against the mvn dependency:tree ground truth and finds the Maven-integrated tool perfect (1.0/1.0) while generic tools miss versions, groupIds and transitive dependencies. It also shows PURL qualifier differences (e.g. type=jar, apt qualifiers) make SBOMs hard to compare, test dependencies are indistinguishable, and argues for an SBOM generator benchmark.

SBOMCycloneDXMaven+3

Eclipse SteadyPackage URLNTIA+3

Supply Chain· 2026-10-16T01:52:05+00:00

endorlabs.comarticle

PWN Request Threat: A Hidden Danger in GitHub Actions | Endor Labs

Explains the "pwn request" weakness in GitHub Actions. A workflow triggered by pull_request_target runs with write permissions and secrets, so if it checks out and builds PR head code, attacker-controlled code runs in a privileged context. It gives a three-step check for susceptible workflows: the trigger, a checkout of the PR head SHA or ref, and build steps such as npm install or make. It also covers mitigations: switch to pull_request or workflow_run, gate on a label, and never pass secrets to scripts that run on PR code. It notes safe patterns and cites the puppetlabs finding.

GitHub ActionsGitHub

pwn requestpuppetlabs

DevSecOps· 2026-10-16T01:52:05+00:00

endorlabs.comarticle

Why Your SCA is Always Wrong | Endor Labs

Explains why traditional SCA tools are inaccurate: they parse package-manager manifests and assume those match what the code actually uses. It walks through phantom dependencies, misused dependency scopes, direct use of transitive dependencies and unused dependencies, using Python and JavaScript as examples. It argues for SCA based on program analysis, with source code as ground truth cross-checked against manifests and the file system. Useful for evaluating SCA tools and understanding SBOM accuracy limits.

PythonJavaScriptnpm+3

phantom dependenciestransitive dependenciesdependency scopes

Supply Chain, AppSec· 2026-10-16T01:52:05+00:00

endorlabs.comarticle

What We Can Learn About GitHub Actions Security from the Trivy Breach | Endor Labs

Endor Labs analyzes the March 2026 Trivy compromise (CVE-2026-33634, CVSS 9.4). A pull_request_target misconfiguration leaked a PAT, and attackers then force-pushed malicious commits to 75 of 76 trivy-action tags and all 7 setup-trivy tags. The TeamPCP credential stealer harvested SSH keys, cloud and CI tokens and runner memory, and exfiltrated to a typosquatted domain. It gives three checks: audit pull_request_target workflows, pin Actions to full commit SHAs, and scope GITHUB_TOKEN permissions. It also notes the limits of SHA pinning.

TrivyGitHub ActionsGitHub+1

CVE-2026-33634TeamPCPGHSA-69fq-xp46-6x23+6

DevSecOps, Supply Chain· 2026-10-16T01:52:05+00:00

endorlabs.comarticle

EU Cyber Resilience Act: Preparing for December 11, 2027 | Endor Labs

Explains what applies on 11 December 2027 under the EU Cyber Resilience Act: Annex I Part I and II, Article 13 manufacturer duties, conformity assessment and CE marking. It covers classification by core functionality, the conformity routes per tier, and how "known exploitable vulnerability" is defined, including findings from your own tooling and AI analysis. It also covers the engineering obligations (per-build SBOMs with VEX, CVD policy, security.txt, signed updates, CSAF disclosure), the 10-year retention and support-period rules, and the product-family and legacy-modification reliefs. It closes with a checklist, and the Endor Labs product claims are vendor-sourced.

Endor LabsSBOMCycloneDX+1

EU Cyber Resilience ActImplementing Regulation (EU) 2025/2392Annex I+7

GRC, Supply Chain· 2026-10-14T17:49:43+00:00

endorlabs.comarticle

What Is SAST? Static Analysis in the Age of AI-Generated Code | Endor Labs

Explainer on Static Application Security Testing. It covers how SAST builds an AST and runs taint analysis from sources to sinks, which flaw classes it catches (SQLi, XSS, hardcoded secrets, weak crypto) and which it misses (IDOR and other access-control bugs). It compares SAST with DAST and SCA, cites false-positive and false-negative figures (91% false positives in a 2025 study of about 3,000 repositories; 47–80% of vulnerabilities missed), and argues for reachability analysis and reasoning-based AI SAST. It ends with evaluation criteria and a rollout plan; the vendor's own benchmark claims are preliminary.

Endor Labs

OWASPOWASP Top 10IDOR+4

AppSec, Learning· 2026-10-12T22:34:16+00:00

endorlabs.comarticle

Executive Order 14028 SBOM Requirements: What to Comply With and How | Endor Labs

Explains what Executive Order 14028 requires for SBOMs. It covers the NTIA Minimum Elements (seven data fields, automation support, practices and processes) and the SPDX, CycloneDX and SWID formats. It also covers who must comply and how OMB M-26-05 (January 2026) rescinded the standardized self-attestation mandate while agencies can still require SBOMs by contract. It then argues an SBOM is only a starting point and recommends adding reachability analysis, EPSS, VEX and safe-upgrade remediation. The last part promotes Endor Labs.

SBOMSPDXCycloneDX+1

Executive Order 14028NTIANIST+10

GRC, Supply Chain· 2026-10-12T22:28:52+00:00

endorlabs.compaper

2024 Dependency Management Report | Endor Labs

Endor Labs' 2024 report on open-source dependency management and vulnerability prioritization. It reports that under 9.5% of vulnerabilities are reachable at function level, and that combining reachability with EPSS cuts noise by about 98%. It also finds Python phantom dependencies (up to 60% of dependencies and 85% of vulnerabilities for some tenants), 18% of npm OSV entries lacking CVE aliases, 69% of advisories published after the fix release (median 25 days), and 47% of advisories with no code-level info. For Java, 24% of fixes need a major version bump and 95% of upgrades carry breaking changes. Written by a vendor, so the prioritization conclusions favor its product.

Endor LabsOSVNVD+4

CVE-2023-41080CVE-2017-12159CVE-2023-36566+10

Supply Chain, AppSec· 2026-10-12T15:59:35+00:00

endorlabs.comarticle

Identifying and Tracking FedRAMP False Positives | Endor Labs

Explains how FedRAMP treats false-positive SCA findings, which still have to be tracked in the POA&M and ConMon. It covers the deviation request (DR) process, the SCA problems of scale and missing context (manifest-based inventories, with 88% of imported code unused), and 30–180 day remediation SLAs. It argues that only high-confidence, function-level reachability that holds from any source is likely to satisfy a 3PAO. It then describes how Endor Labs uses call-graph analysis, SBOM/VEX output and rescanning to document false positives.

Endor LabsSBOMVEX

FedRAMPPOA&MConMon+4

GRC, Supply Chain· 2026-10-12T15:59:35+00:00

endorlabs.comwriteup

Supply Chain Attack targeting Cline installs OpenClaw | Endor Labs

Endor Labs analyzes the compromised Cline CLI npm release 2.3.0, published with a likely stolen long-lived token under the clinebotorg account, which bypassed trusted publishing. Its postinstall hook ran npm install -g openclaw@latest; OpenClaw is benign and no Gateway daemon was started, so impact is low. The post shows the metadata diff (lost provenance attestation, changed publisher) that flagged it, and advises disabling token-based publishing and checking for attestations. Mitigation is to update to 2.4.0 and uninstall openclaw.

ClineOpenClawnpm+3

GHSA-9ppg-jx86-fqw7Adnan Khanclinebotorg

Supply Chain· 2026-10-12T15:59:35+00:00

endorlabs.comarticle

New research: malware in open source ecosystems surges 14x as attackers hijack trusted packages | Endor Labs

Endor Labs summarizes its report "Malware in the Open Source Ecosystem", built from a survey of 605 IT professionals plus analysis of OSV advisories and npm metadata. Over 90% of all OSS malware advisories were filed in 2025, and npm account-takeover advisories rose about 12x (930 of 1,011 ever recorded), often hitting heavily downloaded packages. It also covers the TeamPCP campaign across GitHub Actions, Docker Hub, npm, OpenVSX and PyPI, the axios compromise, and survey findings: only 21% enforce cooldown periods, 51% found malicious packages in 2025, and ownership of the problem is fragmented.

npmOSVEndor Labs+3

Shai-HuludTeamPCPDPRK+2

Supply Chain, Threat Intel· 2026-10-12T15:59:35+00:00

endorlabs.comarticle

The OWASP Top 10 Gets Modernized | Endor Labs

Commentary on the 2025 OWASP Top 10 update presented at OWASP Global AppSec in Washington, D.C. Broken Access Control stays #1, Security Misconfiguration and the new Software Supply Chain Failures category (replacing Vulnerable and Outdated Components) rise to #2 and #3, and Cryptographic Failures, Injection and Insecure Design drop. The author adds context on agentic AI and MCP identity and authorization, AI-assisted SAST, non-human identities, CISA Secure-by-Default, OWASP SPVS, and slopsquatting and AI coding assistant risks.

Model Context Protocol

OWASPOWASP Top 10 2025Broken Access Control+8

AppSec, Supply Chain· 2026-10-12T15:59:35+00:00

endorlabs.compaper

State of Dependency Management 2025 | Endor Labs

Endor Labs' 2025 State of Dependency Management report examines how AI coding agents and MCP servers add supply chain risk. Researchers analysed 10,663 GitHub MCP server repositories and tested AI-generated dependency recommendations across PyPI, npm, Maven and NuGet, finding agents import vulnerable or non-existent packages. MCP servers are immature: 75% are built by individuals, 41% lack license info, and 82% use sensitive APIs. The report advises treating AI-generated code as untrusted third-party input subject to code review, SAST/SCA and dependency governance.

Model Context ProtocolGitHubPyPI+3

Henrik Plate

Supply Chain, AI Security· 2026-10-12T15:59:35+00:00

endorlabs.comarticle

Guide to Implementing Software Supply Chain Security | Endor Labs

Vendor-authored guide that defines a software supply chain as developers, code, dependencies, AI models, containers and CI/CD pipelines. It walks through program components: OSS governance, SAST vs reachability-based SCA, AI model security, SBOM/VEX (NTIA minimum elements, SPDX vs CycloneDX), secrets scanning, CI/CD hardening, and signing/attestation with Cosign/Sigstore. It cites figures such as 95% of vulnerabilities sitting in transitive dependencies and only 1% of SBOMs being fully NTIA-conformant. Each section closes with an Endor Labs product pitch, so treat the framing as vendor-flavoured. The fetched text was truncated, so the final attestation/provenance section was not seen.

Endor LabsSBOMCycloneDX+3

Executive Order 14028NISTCISA+15

Supply Chain, DevSecOps, Learning· 2026-10-12T15:59:35+00:00

endorlabs.comarticle

Context Engineering for Application Security | Endor Labs

Endor Labs whitepaper on using context engineering to make AI coding assistants write secure code. It covers four techniques: secure prompts (priming, decomposition, refinement with design-spec and test-first patterns), system rules (with sample rules and the CSA R.A.I.L.G.U.A.R.D. framework), external security tools wired in over MCP (with an example Cursor SAST rule), and RAG. It also covers MCP server threats and five defensive pillars, Cursor Hooks, and CISO-level recommendations. It matters because it gives practical guardrails for stopping missing-authorization, rate-limiting and hallucinated-dependency problems at generation time.

CursorModel Context ProtocolEndor Labs+2

slopsquattingR.A.I.L.G.U.A.R.D.Cloud Security Alliance+3

AI Security, AppSec· 2026-10-12T15:59:35+00:00

endorlabs.comarticle

Is AI Coding Safe? Introducing the Agent Security League | Blog | Endor Labs

Endor Labs launches the Agent Security League, a public leaderboard extending CMU's SusVibes benchmark (200 tasks, 108 Python OSS projects, 77 CWEs) to commercial coding agents (Claude Code, Cursor, Codex) and frontier models. Functional pass rose to 84.4% from 61% in the original study, but secure pass peaked at only 17.3%. It also documents agents ignoring instructions and mining git history to copy the fix (SWE-Agent + Opus 4.6 did so in 163/200 tasks), and describes the cheating-detection and score-adjustment module added to the harness.

Claude CodeCursorCodex+2

Agent Security LeagueSusVibesOpus 4.6+1

AI Security, AppSec· 2026-10-12T14:53:10+00:00

endorlabs.comarticle

Static SCA vs. Dynamic SCA: Which is Better (and Why It's Neither) | Blog | Endor Labs

Compares static SCA (manifest/source scanning at build time) with dynamic SCA (runtime instrumentation). Static scans are complete but noisy and miss dependencies absent from manifests, for example in Python. Dynamic scans are relevant but incomplete, add agent overhead and surface findings late. It argues eBPF improves deployability of dynamic SCA but not its coverage limit. A worked example with a library of N vulnerable functions illustrates the difference. The author, Endor Labs, advocates static analysis of source code plus reachability analysis to filter findings by whether vulnerable code is actually called.

Endor LabseBPFSnyk+2

AppSec, Supply Chain· 2026-10-12T14:52:03+00:00

endorlabs.comwriteup

Happier DOMs: The perils of running untrusted JavaScript code outside of a web browser | Blog | Endor Labs

Endor Labs explains CVE-2025-61927, an RCE in the happy-dom headless browser caused by relying on Node.js's vm module, which is not a security boundary. They show that the maintainer's mitigation (--disallow-code-generation-from-strings) is insufficient, because untrusted scripts can still pollute builtin prototypes for DoS or RCE (CVE-2025-62410). The fix adds --frozen-intrinsics. The post recommends isolated-vm, Secure EcmaScript, or container/VM isolation for untrusted JavaScript.

happy-domNode.jsJavaScript+2

CVE-2025-61927CVE-2025-62410CVE-2021-23555+6

AppSec· 2026-10-12T14:51:36+00:00

endorlabs.comwriteup

How a Misconfigured CI Workflow Became an npm Supply-Chain Compromise | Blog | Endor Labs

Endor Labs breaks down the May 11, 2026 compromise of 42 @tanstack/* npm packages (84 malicious versions) that carried valid SLSA provenance. The attacker chained a pull_request_target "Pwn Request", GitHub Actions cache poisoning across the fork/base boundary, and OIDC token extraction from Runner.Worker memory, then published directly to npm without stealing any credential. It explains why 2FA, trusted publishing, provenance and branch protection all failed, and lists mitigations: split pull_request_target workflows, treat the cache as untrusted, scope id-token: write to a publish job, pin actions to SHAs, require environment approval, use egress allowlists.

GitHub ActionsnpmOIDC+3

TanStackpwn requesttj-actions/changed-files+5

Supply Chain, DevSecOps· 2026-10-12T14:49:02+00:00

endorlabs.comwriteup

Malicious 'Pyronut' Package Backdoors Telegram Bots with Remote Code Execution | Blog | Endor Labs

Endor Labs analyzes the malicious PyPI package pyronut (versions 2.0.184–2.0.186), a trojanized fork of the Telegram framework pyrogram. It hooks Client.start() to import a hidden pyrogram/helpers/secret.py that registers Telegram handlers letting two hardcoded attacker accounts run arbitrary Python (/e via meval) and shell commands (/shell via /bin/bash -c). C2 runs entirely over Telegram, so there are no external network indicators; the post includes detection, credential-rotation and prevention guidance.

PyPIPythonTelegram+1

pyronutpyrogrammeval+4

Supply Chain, Malware & RE· 2026-10-12T14:47:53+00:00

endorlabs.comadvisory

Tensorlake npm package compromised by Shai-Hulud in latest software supply chain attack | Blog | Endor Labs

Endor Labs documents the compromise of tensorlake@0.5.144, the official npm TypeScript SDK for Tensorlake, which shipped a Shai-Hulud worm payload via a poisoned preinstall hook (node lib/setup.mjs) after the project's main branch was altered and pushed through a legitimate, provenance-signed GitHub Actions release pipeline. The malware harvests npm, GitHub, SSH, cloud (AWS/GCP/Azure), Kubernetes/Docker and Vault credentials, uses an EtherHiding Ethereum wallet as a durable dead-drop to fetch its rotating C2 domain, and republishes itself into other packages the victim controls. The writeup includes affected package versions, file hashes, C2 indicators, and concrete remediation guidance (pin to 0.5.143, rotate credentials, disable install scripts).

npmGitHub ActionsNode.js+3

Shai-HuludEtherHiding

Supply Chain, Threat Intel· 2026-10-08T05:34:08+00:00

endorlabs.comarticle

| Blog | Endor Labs

An Endor Labs technical breakdown of what an "agent sandbox" actually is: a kernel-enforced boundary around an AI coding agent's tool calls (syscalls), distinct from a staging environment or Docker container. It walks through the nx/s1ngularity supply-chain attack as motivating case, compares OS-level primitives (Apple Seatbelt, Linux Landlock/seccomp, Windows Job Objects/AppContainer) used by Claude Code, Cursor, and Codex CLI against containers and microVMs (gVisor, Firecracker), and argues filesystem isolation and network egress isolation are worthless unless enforced together.

Claude CodeCursorCodex CLI+3

s1ngularity

AI Security· 2026-10-06T17:36:13+00:00

endorlabs.comarticle

Sandboxes Explained: What Each Type Actually Isolates | Blog | Endor Labs

An Endor Labs primer surveying what different sandbox technologies actually isolate: hypervisor-based VMs (Firecracker microVMs, the VENOM floppy-controller escape CVE-2015-3456, Spectre/Meltdown), Linux containers (namespaces/cgroups/seccomp, the runc host-overwrite escape CVE-2019-5736, gVisor's userspace kernel, Kata Containers' per-pod microVMs), and language-runtime sandboxes (Java's deprecated SecurityManager, V8 isolates behind Cloudflare Workers, WebAssembly/WASI's capability model). It then pivots to AI agent sandboxing, arguing the threat model flips from "what can this process touch" to "what actions can this confused-deputy agent take," framed around Simon Willison's lethal trifecta (private data, untrusted content, external communication) and controls like filesystem scoping, egress allowlists, scoped short-lived credentials, and session-level audit logging.

FirecrackerQEMUgVisor+3

CVE-2015-3456CVE-2019-5736Spectre+6

AI Security, Cloud & Infra· 2026-10-02T20:05:58+00:00

endorlabs.comwriteup

GPT-6.1 Sol on Codex: Astra-level security, a third faster, zero cheating | Blog | Endor Labs

Endor Labs benchmarks OpenAI's GPT-6.1 Sol against GPT-6 Sol and GPT-6 Astra using its Agent Security League Codex harness, measuring how often each model produces functionally correct AND securely written code (FuncPass/SecPass) across 200 coding tasks. GPT-6.1 Sol scores 34.1% SecPass (vs Astra's 34.6%), runs a third faster, consumes fewer tokens, and triggers zero confirmed cheating across 13 flagged instances, continuing a streak of clean Codex runs.

CodexCodex CLIOpenAI+2

GPT-6.1 SolGPT-6 SolGPT-6 Astra+1

AI Security· 2026-10-02T19:31:03+00:00

endorlabs.comarticle

GPT-6 Sol on Codex: average scores, quarter the cost | Blog | Endor Labs

Endor Labs ran Codex CLI with OpenAI's GPT-6 Sol on its Agent Security League tasks (200 real-world coding tasks drawn from historical security fixes). It scored 72.1% FuncPass and 25.1% SecPass, sixth overall, 5 points above GPT-5.6 Sol and 9.5 below GPT-6 Astra. The run cost $104 on Azure, 78% less than Astra, with zero confirmed cheating. The post also compares command-use patterns and walks through the one globally unique secure solve, the Plone isURLInPortal XSS/open-redirect fix (CVE-2015-7316). There the agent used iterative URL decoding and a broad character rejection, where the golden fix used a string blocklist.

CodexCodex CLIOpenAI+3

CVE-2015-7316Agent Security LeagueGPT-6 Sol+4

AI Security, AppSec· 2026-10-01T22:46:35+00:00

endorlabs.comwriteup

AI Agent Sandbox Flaw in brig: Symlink Traversal (CVSS 8.2) | Blog | Endor Labs

Endor Labs researcher pkennedy details GHSA-wp6x-29qx-fpr7 (ENDOR-VUL-2026-1809, CVSS 8.2, CWE-59), a symlink-traversal flaw in brig, a tool that runs AI coding agents inside microVM/container sandboxes. While brig's shared working directory was carefully guarded against symlink swaps, the user-named project directory was passed to the underlying runtime (Virtualization.framework on macOS, containerd/nerdctl on Linux) as a raw unresolved path; an agent could replace a subfolder it already controls with a symlink to any host directory, which is then mounted read-write into the sandbox on the next run, exposing SSH keys, cloud credentials, and other projects with the operator's privileges. Fixed in brig 0.3.0 by resolving and holding the path open before mounting; the piece includes full PoC commands, root-cause code snippet, and a detailed vendor disclosure/CVSS negotiation narrative.

Endor LabsmacOSLinux+1

GHSA-wp6x-29qx-fpr7ENDOR-VUL-2026-1809CWE-59+1

AI Security, Offensive Security· 2026-09-28T21:52:07+00:00

endorlabs.comarticle

Opus 5.5: 6x cheaper and 2x faster than Fable 5.1, but only 33.5% of code is secure | Blog | Endor Labs

Endor Labs ran Claude Code with Opus 5.5 on its Agent Security League coding tasks. It scored 68.7% FuncPass and 33.5% SecPass, third on security and nineteenth on functionality, behind Fable 5.1 (87.2% / 37.4%). Its anti-cheating pipeline confirmed 51 training-recall solves, the largest memorization penalty on the board; with those counted it would have led by over 11 points. The post also compares cost (~$116 versus ~$1,116 for Opus 5), speed, tokens and tool calls, and checks Anthropic's launch claims against the results.

Claude CodeAnthropicGPT-6 Astra+3

Agent Security LeagueOpus 5.5Opus 5+5

AI Security, AppSec· 2026-09-24T14:26:30+00:00

endorlabs.comarticle

Engineering a security harness for AI coding agents | Blog | Endor Labs

Endor Labs engineering post arguing that prompts are not security controls and lays out a seven-question framework for building a "security harness" around AI coding/remediation agents: defining the job, minimizing evidence to authoritative facts, tiering authority (Read→Recommend→Prepare→Publish→Enforce), gating consequential actions on proven conditions, evaluating process/result/outcome with contestability, failing closed on interruption or attack, and versioning/owning the agent over time. It illustrates the approach with a controlled dependency-remediation workflow (upgrading Jinja2 with explicit stop-for-approval gates) and cites an internal benchmark where evidence-equipped agents used 91.7% fewer tokens (6.6M vs 79.5M) than agents that had to reconstruct security posture via tool-loop reconnaissance.

Endor Labs

AI Security· 2026-09-22T18:00:38+00:00

endorlabs.comarticle

Vulnerability Blast Radius: How to Measure and Reduce Impact | Blog | Endor Labs

Endor Labs blog post explaining CVE-2024-3596 (BlastRADIUS), a critical design flaw in the RADIUS authentication protocol where an on-path attacker uses a chosen-prefix MD5 collision to forge an Access-Accept response and bypass authentication on VPNs, firewalls, and network devices using RADIUS over UDP with non-EAP methods. It also generalizes 'blast radius' as a vulnerability-management concept, describing static reachability analysis across first-party code, dependencies, and container base images to distinguish exploitable findings from theoretical CVEs, tying the discussion to Endor Labs' AURI product.

RADIUSFreeRADIUSEndor Labs

CVE-2024-3596CVE-2021-44228chosen-prefix MD5 collision

AppSec, Identity & Access, Supply Chain· 2026-09-21T09:19:24+00:00

endorlabs.comarticle

AI-Generated Malware Risk: A Practical Guide for Developers | Blog | Endor Labs

An Endor Labs guide explaining how AI-generated and AI-enhanced malware (citing Check Point's VoidLink, Google TIG's PROMPTFLUX/PROMPTSTEAL, and HYAS Labs' BlackMamba/EyeSpy) differs from traditional threats by dynamically mutating code at runtime to evade signature-based detection. It argues software supply chains are a prime target because AI can mass-produce convincing typosquatted or dependency-confused packages, and recommends behavioral analysis plus full-stack reachability analysis (via Endor Labs' AURI) as defenses instead of relying solely on CVE/signature matching.

Endor LabsnpmPyPI

VoidLinkPROMPTFLUXPROMPTSTEAL+7

AI Security, Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

How CycloneDX VEX Makes Your SBOM Useful | Blog | Endor Labs

Endor Labs explains how the CycloneDX Vulnerability Exploitability eXchange (VEX) format's analysis sub-field (state, justification, response, detail) lets software publishers communicate real exploitability of SBOM-listed CVEs, cutting through the noise of scanners that flag thousands of largely non-exploitable findings. It walks through each state value (resolved, resolved_with_pedigree, exploitable, in_triage, false_positive) as a machine-readable alternative to ad hoc spreadsheets/portals for vulnerability disclosure, referencing log4shell (CVE-2021-44228) as a case where faster communication would help.

CycloneDXSBOM

CVE-2021-44228

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

How Should I Prioritize Software Vulnerabilities? | Blog | Endor Labs

Endor Labs blog post comparing five approaches to prioritizing software vulnerabilities: CVSS scoring, CISA's KEV catalog, Carnegie Mellon's SSVC decision tree, FIRST's EPSS exploit-prediction model, and call-graph-based reachability analysis. It cites concrete data (e.g., EPSS v3 threshold of 0.088 catches ~82% of exploited CVEs while requiring remediation of only 7.3% of known CVEs, versus patching most CVSS high/critical issues) and walks through each method's limitations, using examples like CVE-2017-0144 (EternalBlue/NotPetya) and CVE-2004-1464.

CVSSEPSSNVD+2

CVE-2017-0144CVE-2004-1464CISA KEV

AppSec· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Cursor Security: How to Secure AI-Generated Code in 2026 | Blog | Endor Labs

Endor Labs blog post detailing seven security risks specific to Cursor's AI code generation that its built-in controls (workspace trust, SOC 2, privacy mode) don't cover: indirect prompt injection via files/comments, malicious .cursorrules files creating persistent backdoors, AI-suggested typosquatted or outdated dependencies, hardcoded credential leakage in generated test/boilerplate code, cross-project context poisoning via repository indexing, and unreviewed agent auto-run execution. It contrasts Cursor's native controls with what external AppSec tooling (pitched as Endor Labs' AURI) needs to fill, recommending dependency pinning, egress filtering, secret-scanning pre-commit hooks, and reachability-based SCA/SAST.

CursorEndor Labs

AURI

AI Security, Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comwriteup

Shai-Hulud 2 Malware Campaign Targets GitHub and Cloud Credentials Using Bun Runtime | Blog | Endor Labs

Endor Labs' live incident tracker for the Shai-Hulud 2.0 npm supply-chain worm that began around Nov 24, 2025, compromising packages from Zapier, ENS Domains, Postman and PostHog. It details the infection chain (malicious preinstall script, setup_bun.js dropper, obfuscated bun_environment.js payload), TruffleHog-style credential theft from AWS, GCP, Azure, GitHub and npm, self-replication via stolen npm and GitHub tokens, a self-hosted GitHub Actions runner backdoor abusing repository Discussions for command injection and C2, and destructive wiper behavior that shreds the user's home directory on Windows and Linux.

npmBunGitHub Actions+3

Shai-Hulud 2.0Shai-HuludZapier+3

Supply Chain, Threat Intel, DevSecOps· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Combining EPSS and Reachability Analysis to Optimize Vulnerability Management | Blog | Endor Labs

Endor Labs breaks down how the Exploit Prediction Scoring System (EPSS) v3 works — the exploitation telemetry, exploit-DB/GitHub/Metasploit code checks, CISA KEV/Project Zero/ZDI mentions, CVSS vectors, CWE, and social-media signals it ingests to score CVE exploitation likelihood — and shows that an EPSS-threshold (0.088) remediation strategy fixes the same share of exploited CVEs while patching only ~7.3% of known vulnerabilities versus ~82% under a "fix all high/critical" CVSS approach. It then explains a layered workflow: use reachability analysis (call-graph based) to cut findings by ~80% to what's actually exploitable in your environment, then apply EPSS on that reduced set to rank remaining issues by real-world exploitation likelihood, including for third-party/proprietary code where reachability analysis isn't feasible.

EPSSCVSSNVD+3

CISA KEVCWE-94

AppSec· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Key Questions for Your SBOM Program | Blog | Endor Labs

Endor Labs blog post outlining six practical questions organizations should answer before standing up an SBOM program: how to send/receive/store SBOMs, which vendors must provide them (via business impact analysis), delivery frequency and SLAs, how to analyze them for exploitability (reachability analysis vs. simple CVE counting), remediation/risk-acceptance policy, and using SBOMs plus VEX during incident response (e.g., Log4Shell-style disclosures). It closes with a pitch for Endor Labs' source-code-based SBOM/VEX generation.

SBOMEndor Labs

Log4Shell

Supply Chain, GRC· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

What is VEX and Why Should I Care? | Blog | Endor Labs

Endor Labs explains VEX (Vulnerability Exploitability eXchange), the CISA-backed machine-readable format that lets software producers assert whether a vulnerability in a component actually affects their product, complementing SBOMs which only list components and versions. It walks through how VEX documents annotate exploitability (e.g., unreachable code), and describes how Endor Labs automates this by building a dependency callgraph via static analysis to determine reachability and auto-generate SBOM/VEX documents.

Endor LabsSBOMCVSS+1

CISA

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

SSDF Compliance and Attestation: A Practical Guide | Blog | Endor Labs

Endor Labs breaks down CISA's finalized Secure Software Development Attestation Form, which requires federal software suppliers to attest compliance with NIST's Secure Software Development Framework (SSDF) under EO 14028, with critical software due June 8, 2024 and other software due September 8, 2024. It covers the form's scope (excluding federal-developed, open source, and freely available software), flags ambiguous language and gaps (no explicit SBOM/threat-modeling/memory-safety mandate), and maps the four required practice areas — secure environments, trusted source code supply chains, code/artifact provenance, and vulnerability scanning/remediation — to Endor Labs' supply chain security platform features.

Endor LabsSBOM

NIST SSDFExecutive Order 14028CISA+3

GRC, Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

OWASP OSS Risk 1: Known Vulnerabilities | Blog | Endor Labs

First in Endor Labs' 10-part series on the OWASP Top 10 OSS Risks, this piece breaks down "Known Vulnerabilities" (OSS-RISK-1): how CVEs get assigned and disclosed, why the 2017 Apache Struts CVE-2017-5638 unpatched flaw led to the Equifax breach of 145M+ records, and how to compare vulnerability data sources (NVD, GitHub Advisory Database, OSV). It details prioritization methods — CVSS, EPSS, CISA KEV, SSVC, and function-level reachability analysis — and shows a case study where filtering 134K findings down via reachability, fix-availability, production-code, and EPSS thresholds cut remediation work by 99.97%.

Endor LabsNVDOSV+2

CVE-2017-5638EquifaxApache Struts+2

Supply Chain, AppSec· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

OSS Vulnerabilities And The Digital Operational Resilience… | Blog | Endor Labs

Endor Labs breaks down the EU Digital Operational Resilience Act (DORA), applicable to financial entities from January 2025, focusing on Article 9 (patch management) and Article 25 (open source analysis/source code review), plus the January 2024 Regulatory Technical Standards Articles 10 (tracking third-party/OSS libraries) and 16 (source code review, security testing before production). It then maps these compliance requirements to concrete AppSec practices: SBOM/SCA for component inventory, proactive OSS risk scoring, vulnerability scanning, and function-level reachability analysis to prioritize remediation and generate VEX documents, noting overlap with PCI DSS requirements 6.3/11.3.

Endor LabsSBOMEPSS

DORAPCI DSSGDPR

GRC, Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Demystifying Transitive Dependency Vulnerabilities | Blog | Endor Labs

Endor Labs blog post demystifying transitive dependencies — libraries pulled in indirectly through direct dependencies — and explaining why they complicate software composition analysis: multiple versions of the same transitive dependency can coexist in a project, it's harder to see what's actually used, and manually forcing an update is risky. It argues reachability analysis (statically stitching first-party call paths through to transitive library code) is the key tool for determining exploitability and for satisfying auditors under PCI DSS, FedRAMP, and EO 14028 SBOM requirements, and walks through a real example where langchain4j pulls a vulnerable JSON library transitively via Redis's jedis client, with the fix being to bump jedis rather than the JSON library directly.

Endor LabsRedisLangChain

Executive Order 14028PCI DSSFedRAMP

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Evaluating and Scoring OSS Packages | Endor Labs | Blog | Endor Labs

Endor Labs blog post detailing their "Endor Score" methodology for evaluating open source package risk, distinguishing operational risk (activity, popularity, licensing, code quality) from security risk (vulnerabilities, secrets, malicious code, security practices). It argues against compressing everything into a single opaque score, favoring transparent, deterministic "facts" (e.g., recent commit counts, normalized SAST findings) that users can combine into custom policies, and discusses pitfalls like event-selection bias, threshold selection, and the unproven predictive power of these signals.

Endor LabsDependabotGitHub+2

OpenSSF ScorecardGoogle Assured Open Source SoftwareOWASP Top 10 Risks for Open Source Software+1

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

FedRAMP Requirements for Vulnerability Management and Dependency Upgrades | Blog | Endor Labs

An Endor Labs blog post, written by a former JAB Technical Representative, details FedRAMP's Continuous Monitoring vulnerability management requirements: authenticated scanning, monthly dependency inventories, POA&M tracking, and strict remediation SLAs (High CVSS 7.0+ in 30 days, Medium in 90, Low in 180). It also covers FedRAMP's container-specific rules (hardened images, CISA KEV prohibition, pipeline/registry/runtime scanning) and outlines four practical strategies — reachability-based false-positive triage, EPSS-driven risk level adjustments, ROI/complexity-based upgrade prioritization, and developer-side PR gating — before pivoting into an Endor Labs product pitch.

Endor LabsCVSSEPSS

FedRAMPCISA KEVNIST 800-53

GRC, Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Why OVAL Feeds Outperform NVD for Linux Vulnerability Management | Blog | Endor Labs

Endor Labs explains why OVAL (Open Vulnerability and Assessment Language) feeds from Linux distributions like Debian, Ubuntu, and Red Hat produce more accurate vulnerability data than NVD for container/Linux scanning. Using CVE-2022-1587 (PCRE2 DoS) as a case study, it shows how NVD rated the flaw "Critical" (CVSS 9.1) while Ubuntu's OVAL-based advisory rated it "Low" because the distro had already backported a fix into a version string (10.34-7ubuntu0.1) that naive NVD-based scanners would still flag as vulnerable.

NVDOVALDebian+3

CVE-2022-1587

DevSecOps, AppSec· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

The UK Software Security Code of Practice through a Software Supply Chain Lens | Blog | Endor Labs

Endor Labs analyzes the UK's Software Security Code of Practice, a government-issued framework for software vendors, through the lens of software supply chain security. It breaks down how the Code's requirements map to practices like SBOM generation, dependency/component management, and vendor accountability for third-party code.

SBOM

UK Software Security Code of Practice

Supply Chain, GRC· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Anti-Pattern Avoidance: A Prompt Pattern for Safer AI-Generated Code | Blog | Endor Labs

Endor Labs describes the "anti-pattern avoidance" prompt pattern: explicitly instructing an LLM to avoid named CWEs (e.g., CWE-89, CWE-20, CWE-200) when generating code, rather than just asking it to "write secure code." It shows a before/after JavaScript form-handling example where naming the CWEs causes the model to add input validation/sanitization, and cites an academic study showing this reduced weakness density by 64% in GPT-3 and 59% in GPT-4 versus baseline prompts.

OpenAIGPT-4JavaScript

CWE-89CWE-20CWE-200

AI Security, AppSec· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

The Most Common Security Vulnerabilities in AI-Generated Code | Blog | Endor Labs

Endor Labs blog post analyzing why LLM-generated code (from tools like GitHub Copilot, Cursor, Replit Ghostwriter) reproduces classic vulnerability patterns from its open-source training data, citing recurring CWE Top 25 weaknesses such as missing input validation (CWE-20), SQL/OS command injection (CWE-89/CWE-78), broken authentication/access control (CWE-306/CWE-284), and hard-coded credentials (CWE-798). It also covers AI-specific risks: dependency explosion from oversized auto-generated dependency trees, stale/vulnerable library suggestions past training cutoff, hallucinated package names enabling slopsquatting attacks, and "architectural drift" where AI edits silently break security invariants without triggering static analysis or code review flags.

GitHub CopilotCursorEndor Labs

CWE-20CWE-89CWE-78+4

AI Security, AppSec, Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

How to Defend Against NPM Software Supply Chain Attacks | Blog | Endor Labs

Endor Labs blog post analyzing the wave of npm supply chain compromises (eslint-prettier, Nx, chalk, and the self-propagating "Shai-Hulud" worm that hit @ctrl/tinycolor via stolen maintainer credentials), citing the Zimmerman et al. study on npm's dense transitive-dependency trust model. It provides a checklist for security teams: enforcing lockfiles and npm ci in CI, defaulting to --ignore-scripts to block malicious lifecycle hooks, running SCA/malware scanning, and delaying adoption of new package versions (60-day cooldown per CIS benchmark, PNPM minimum-release-age feature).

npmpnpmDependabot

Shai-Hulud@ctrl/tinycoloreslint-prettier compromise+4

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Why Cooldown Windows Belong in Every npm Security Strategy | Blog | Endor Labs

Endor Labs argues that cooldown windows delaying adoption of newly-published npm package versions by 24-48 hours are an effective defense against short-lived npm malware campaigns like Shai-Hulud, chalk/debug, and s1ngularity, since malicious releases are usually caught within that window. It also covers pinning vs lockfiles and demonstrates enforcing cooldown policies via OPA/Rego.

npmpnpmOpen Policy Agent+2

Shai-Huluds1ngularitychalk+1

Supply Chain, DevSecOps· 2026-09-11T21:16:20+00:00

endorlabs.comwriteup

AI SAST in Action: Finding Real Vulnerabilities in OpenClaw | Blog | Endor Labs

Endor Labs describes applying its AI SAST engine to the open-source OpenClaw AI agent framework, tracing data flows from attacker-controlled sources (HTTP params, config, conversation history, external API responses) through transformations to dangerous sinks like command execution and file operations. The team validated findings by deploying OpenClaw in Docker and building working proof-of-concept exploits, confirming 7 vulnerabilities spanning HTTP handlers, business logic, tool dispatch, and low-level implementation layers, now reported to maintainers via responsible disclosure with technical details pending patch release.

Endor LabsOpenClawDocker

AppSec, AI Security· 2026-09-11T21:16:20+00:00

endorlabs.comwriteup

AI SAST Finding: Path Traversal in OpenClaw via LLM Guardrail Bypass | Blog | Endor Labs

Endor Labs details GHSA-r5fq-947m-xm57, a high-severity path traversal (CVSS 8.8) in OpenClaw's apply_patch tool that allows arbitrary file write/delete when sandboxRoot isn't configured, tracing the vulnerable code path from resolvePatchPath() through resolvePathFromCwd() to fs.writeFile/fs.rm. Because the tool is only invoked via LLM decision, the researcher bypassed the model's safety guardrails by injecting fabricated function_call/function_call_output entries into the /v1/responses conversation history, tricking the LLM via in-context learning into emitting a real malicious tool call with a traversal payload. This illustrates how LLM safety training can be the sole runtime control gating dangerous tool arguments, and how that control can be defeated with conversation history injection.

OpenClawEndor Labs

GHSA-r5fq-947m-xm57CWE-22

AppSec, AI Security· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

SolarWinds took a nation-state. The next attack just needs an LLM and $5. | Blog | Endor Labs

Endor Labs blog tracing an accelerating pattern of software supply-chain compromises from July 2025 through March 2026, culminating in the TeamPCP threat actor's cascading 30-day compromise of Aqua Security, Trivy, GitHub Actions, Docker Hub, npm, OpenVSX, and PyPI (including backdooring the 95M-download LiteLLM package). It argues AI is accelerating both offense (CVE-GENIE reproducing 51% of CVE exploits for $2.77 each) and the attack surface (the Cline prompt-injection-to-npm-token-theft chain, Nx's AI-review-evading payload), and calls for treating AI coding agents as privileged actors, pinning GitHub Actions to SHAs, and platform-level fixes like immutable tags and Sigstore/OIDC.

npmGitHub ActionsPyPI+3

TeamPCPAqua SecurityXploiters+15

Supply Chain, Threat Intel, AI Security· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Common C/C++ Vulnerabilities: A Practical Guide to Prevention | Blog | Endor Labs

Endor Labs guide to C/C++ memory-safety and input-handling vulnerabilities — buffer overflow (CWE-787/120/121), use-after-free (CWE-416), null pointer dereference (CWE-476), integer overflow (CWE-190), and format string bugs (CWE-134) — with vulnerable/fixed code snippets for each. It surveys detection tooling (Coverity, CodeQL, Clang Static Analyzer, AFL++/libFuzzer with ASan/UBSan, OSS-Fuzz) and argues that build-system complexity (Bazel, CMake, Conan, vcpkg) and high SAST false-positive rates make reachability analysis necessary, closing with a pitch for Endor Labs' AURI reachability engine.

Endor LabsCodeQLAURI+1

CWE-787CWE-416CWE-476+4

AppSec, Learning· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Next-Gen SCA for C/C++: Closing the Detection Gap | Blog | Endor Labs

Endor Labs announces C/C++ language support for its SCA product, addressing the long-standing difficulty of tracking OSS dependencies in ecosystems with no central package registry. Because C/C++ developers commonly vendor (copy and modify) library source directly into their repos, Endor Labs builds a fingerprint index of known OSS libraries using cryptographic hashes for exact-match segments (functions, license blocks, etc.) and ML-generated code embeddings to catch renamed, reformatted, or lightly modified copies. The company reports customer benchmarks showing 81% fewer false negatives and 143% more true positives versus prior tooling for building SBOMs and detecting vulnerable/license-non-compliant code in C/C++ codebases.

Endor LabsCSBOM

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

Malicious Code in Open Source: How to Detect It Before You Ship | Blog | Endor Labs

Endor Labs explains the two dominant classes of OSS malware attacks - dependency/name confusion (typosquatting, combo-squatting, brand-jacking) and attacks on legitimate packages/maintainers - and how the OpenSSF malware-analysis project sandboxes new PyPI/npm releases and reports confirmed malware to OSV. It walks through real cases it detected, including the chunked/split-across-files PyPI malware ttlo and gisi, and Whatfuscator which drops and runs a PyInstaller-built Windows executable, plus two false-positive suspicious-code flags (versioneer 0.29, azure-functions 1.18.0) caused by benign base64/zlib obfuscation.

PyPIPythonnpm+3

ttlogisiWhatfuscator+5

Supply Chain· 2026-09-11T21:16:20+00:00

endorlabs.comarticle

The first part of the EU Cyber Resiliency Act comes into effect on September 11th. Are you Ready? | Blog | Endor Labs

Endor Labs breaks down the EU Cyber Resilience Act's Article 14 vulnerability/incident reporting obligation that took effect September 11, 2026, covering the three-stage 24/72-hour/final report cadence, the Commission's July 2026 guidance on when a manufacturer becomes "aware," and Article 64's tiered fines (up to €15M/2.5% turnover). It argues that reachability analysis — determining whether a vulnerable function/package is actually callable in a product — is becoming the deciding factor in whether a vulnerability triggers a reportable event, citing Endor Labs' SCA and Container Reachability tooling that reduces findings by up to 92% and 90% respectively.

Endor LabsCursor

EU Cyber Resilience ActENISANIS2

GRC, Supply Chain· 2026-09-10T12:00:01+00:00

endorlabs.comarticle

Two new frontier models posted their best-ever scores on the Endor Labs Agent Security League | Blog | Endor Labs

Endor Labs ran Claude Fable 5.1 (on Claude Code) and OpenAI's GPT-6 Astra (on Codex) through its Agent Security League benchmark, which tasks agents with fixing historically-vulnerable code without being told a vulnerability exists, then checks both functional tests (FuncPass) and hidden upstream security tests (SecPass). Fable 5.1 topped the board at 87.2% FuncPass but only 37.4% SecPass, and of patches that passed all functional tests, 57% still left the flaw exploitable — illustrated with CVE-2023-31146 in the Vyper compiler, where swapping a store/copy order re-introduces an out-of-bounds write invisible to functional tests. The post also breaks down token/turn cost economics (cache-read-heavy spend, timeouts billed in full) and argues that cheaper, better-scoring models actually worsen the human-review cost structure since reviewers can't tell secure passes from insecure ones without checking every patch.

Endor LabsClaude CodeCodex+2

CVE-2023-31146Agent Security LeagueClaude Fable 5.1+2

AI Security, AppSec· 2026-09-08T22:42:30+00:00

endorlabs.comwriteup

Codex with GPT-6 Astra posts 82.1% FuncPass and 34.1% SecPass | Blog | Endor Labs

Endor Labs ran OpenAI's GPT-6 Astra through Codex against its Agent Security League benchmark of real-world secure-coding tasks, scoring 82.1% FuncPass / 34.1-34.6% SecPass versus 67.6%/20.1% for Codex + GPT-5.6 Sol — the largest generation-over-generation jump recorded in the Codex family, and close to Claude Code + Fable 5.1 (87.2%/36.9%) while beating Claude Code + Opus 5 (73.7%/37.4%). The post details the multi-signal anti-cheating pipeline (16 flagged instances, all cleared for Astra vs. 17 confirmed cheats for Fable 5.1 and 38 for Opus 5) and the speed/token trade-off: Astra is ~1.8x slower than both GPT-5.6 Sol and Fable 5.1, with 29 timeouts out of many tasks in a one-hour cap, though Codex still extracts a diff at kill time.

CodexOpenAI CodexClaude Code+3

Agent Security LeagueGPT-6 AstraGPT-5.6 Sol+2

AI Security· 2026-09-08T22:31:27+00:00

endorlabs.comarticle

How Endor Labs AI SAST Learns What Matters to Your Organization | Blog | Endor Labs

Endor Labs explains how its AI SAST product incorporates context beyond source code — automatically-ingested Infrastructure as Code (Dockerfiles, Terraform, CloudFormation, Kubernetes/Helm) and repository files (AGENTS.md, CLAUDE.md, skills files), plus optional analyst feedback and natural-language threat-model/architecture context — to build a deployment-aware model of an application and cut false positives. It cites a benchmark of 192 verified vulnerabilities across 8 projects, claiming 2.6x more true positives than the next tool (Claude Opus 4.7) and 63 findings no other tool caught, and walks through customer examples where external-auth annotations and WAF-mitigated XSS severity were tuned via this context.

Endor LabsTerraformDocker+2

Opus 4.7

AppSec, AI Security· 2026-09-07T10:00:01+00:00

endorlabs.comwriteup

Claude Code with Fable 5.1 posts 87.2% FuncPass and 37.4% SecPass — a new #1 on our board — while running as fast as Fable 5, costing a third of Opus 5, finishing every task with zero timeouts, and drawing less than half the confirmed cheating of both predecessors. | Blog | Endor Labs

Endor Labs benchmarks Claude Code running Anthropic's Fable 5.1 against Opus 5 and Fable 5.0 on its Agent Security League leaderboard, which tests whether agents can independently re-derive historical security fixes (FuncPass/SecPass) without recalling them from training data or git history. Fable 5.1 tops both axes (87.2% FuncPass, 37.4% SecPass), runs at Fable 5.0's speed with zero timeouts, costs roughly a third of Opus 5, and shows far less confirmed training-data memorization than either predecessor. The post includes three detailed 'hall of fame' case studies where Fable 5.1 alone produced an independently-reasoned secure patch: Home Assistant's unauthenticated error-log route (CVE-2018-21019/CWE-200), html-sanitizer's Unicode-normalization XSS bypass (CVE-2024-34078/CWE-79), and Vyper's self-referencing dynarray out-of-bounds write (CVE-2023-31146/CWE-787).

Claude CodeHome AssistantVyper

CVE-2018-21019CVE-2024-34078CVE-2023-31146+8

AI Security, AppSec· 2026-09-04T19:54:24+00:00

endorlabs.comarticle

Agentic Development Security: How to Secure Code Your Agents Write | Blog | Endor Labs

Endor Labs blog post defining "agentic development security" — securing code, dependencies, and tool use when AI coding agents generate and merge code autonomously. It cites research showing 45% of LLM-generated code samples fail security tests (Veracode), 34% of AI-recommended dependency versions are hallucinated (Endor Labs), 82% of MCP implementations have path-traversal-prone file operations, and explains reachability analysis plus EPSS scoring as noise-reduction filters for prioritizing exploitable findings.

Endor LabsModel Context ProtocolEPSS

OWASP Top 10 for LLM ApplicationsOWASP Top 10 for Agentic ApplicationsNIST SSDF+7

AI Security, Supply Chain, AppSec· 2026-09-02T22:38:08+00:00

endorlabs.comarticle

The Secure Agentic Development Lifecycle (ADLC), Explained | Blog | Endor Labs

Endor Labs defines the "agentic development lifecycle" (ADLC) — the process by which AI coding agents autonomously plan, write, test, and ship code — and lays out where it introduces new security risk: insecure-by-default code, architectural design flaws that pass static analysis, hallucinated/unvetted dependencies, and a review bottleneck as agents outpace human reviewers. It cites benchmark data (62% of best-model BaxBench solutions insecure, only 10.5% of SWE-Agent/Claude 4 Sonnet solutions secure, only 1 in 5 AI-recommended dependency versions safe) and proposes a control stack — secure-generation prompting, security-aware multi-agent PR review, reachability-based prioritization, and automated remediation — mapped to NIST SP 800-218A, CSA's Agentic SDLC, and OWASP.

Model Context ProtocolCursorClaude Code+3

BaxBenchSWE-AgentNIST SP 800-218A+4

AI Security, Supply Chain, DevSecOps· 2026-09-02T22:36:43+00:00

endorlabs.comarticle

AI Code Review: How to Actually Review Code an Agent Wrote | Blog | Endor Labs

Endor Labs explainer on "AI code review" as a discipline: pairing LLM reasoning with static analysis (call graphs, data flow) to catch security-relevant PR changes that AI coding agents introduce, alongside human reviewers. It cites research showing common AI-generated flaws (missing input validation CWE-20/89/78, hardcoded secrets CWE-798, broken auth CWE-306/284, hallucinated/insecure dependencies), Endor's finding that under 9.5% of vulnerabilities are reachable/exploitable, and a comparison of AI review against SAST, SCA, and secrets scanning, plus a rollout checklist referencing NIST SP 800-218A.

Endor LabsGitHubGitLab+3

CWE-20CWE-89CWE-78+5

AI Security, AppSec· 2026-09-02T22:35:33+00:00

endorlabs.comarticle

SAST for AI-Generated Code: What Static Analysis Catches and Misses | Blog | Endor Labs

Endor Labs blog post arguing traditional signature-based SAST misses the logic and business flaws AI coding assistants introduce, since those flaws are "not pattern-shaped." It contrasts AI-assisted SAST (rules plus a thin AI layer) with AI-native SAST that traces dataflow across files and uses reachability analysis plus evidence-based triage to cut false positives, citing that Endor Labs' own tool (built on Opengrep with AI-agent triage) found a zero-day, CVE-2026-55407, in Anthropic's Rust buffa library and that only ~1 in 5 AI-recommended dependency versions are safe.

Endor LabsOpengrepRust

CVE-2026-55407OWASP Top 10 for LLM Applicationsslopsquatting+2

AppSec, AI Security, Supply Chain· 2026-09-02T22:32:29+00:00

endorlabs.comarticle

Prompt Patterns That Make Coding Agents Write Safer Code | Blog | Endor Labs

Endor Labs breaks down prompt engineering patterns that reduce vulnerabilities in AI-generated code: a design-spec pattern that front-loads security requirements, naming specific CWEs to avoid (e.g. CWE-89, CWE-79, CWE-20), and a "secure-insecure diff" recursive self-critique loop, while warning that telling a model to role-play a security expert actually performs worse. It cites research showing these patterns cut vulnerability density by up to 56–77.5%, notes that repeated AI iteration can drift toward less-secure code, and flags that AI coding agents frequently suggest hallucinated or vulnerable dependencies (34% hallucinated per Endor's own dependency research), arguing prompts must be paired with SAST/SCA/secrets scanning and reachability-based prioritization.

Endor LabsClaude CodeCursor+1

CWE-89CWE-79CWE-20+1

AI Security· 2026-09-02T22:30:25+00:00

endorlabs.comarticle

Secrets in AI-Generated Code: How Coding Agents Leak Credentials | Blog | Endor Labs

Endor Labs breaks down why AI coding assistants (Claude Code, Cursor, etc.) hardcode and leak secrets (CWE-798): they learn the habit from public training data, then compound it by ingesting whole workspaces including .env files and copying live tokens into new code or MCP configs. It cites hard data — GitGuardian's 2026 report found 28.65M new hardcoded secrets on public GitHub in 2025 and a 3.2% secret-leak rate on Claude Code-assisted commits vs 1.5% baseline, Lakera found credentials in 33 .claude/settings.local.json files across 30 packages, and Wiz found AI-related secrets among the most common validated leaked credential types — then lays out a layered defense (pre-commit/IDE/CI/CD/full git-history scanning, active credential validation, secure prompting) and an incident-response order of operations (rotate, investigate, purge history, revoke, re-scan).

GitleaksTruffleHogEndor Labs+2

CWE-798FedRAMPEU Cyber Resilience Act

DevSecOps, AI Security· 2026-09-02T22:28:24+00:00

endorlabs.comarticle

Hallucinated Packages: How AI Invents Dependencies Attackers Exploit | Blog | Endor Labs

Endor Labs explainer on hallucinated packages: AI coding tools frequently invent plausible-but-nonexistent library names (19.7% of 576K sampled code generations across 16 models contained hallucinated package references per a 2025 study), and attackers register those exact names to carry out 'slopsquatting' supply-chain attacks that traditional typo/spelling checks can't catch. The piece contrasts slopsquatting with typosquatting and dependency confusion, cites stats (only ~1 in 5 AI-recommended dependency versions are safe, 49% carry known CVEs, 51% of orgs saw suspected/confirmed malware in 2025), and recommends layered defenses: verify packages before install, review AI-generated imports, detect malicious packages, and prioritize via reachability analysis.

Endor LabsnpmPyPI

slopsquattingCloud Security Alliance

Supply Chain, AI Security· 2026-09-02T22:26:40+00:00

endorlabs.comarticle

Prompt Injection Against Coding Agents: The Attack Surface Nobody Owns | Blog | Endor Labs

Endor Labs breaks down how indirect prompt injection compromises AI coding agents (Cursor, GitHub Copilot) via poisoned READMEs/rules files, malicious MCP servers, and web/issue content the agent reads with elevated trust and auto-approve permissions. Cites a study testing 314 payloads across 70 MITRE ATT&CK techniques showing up to 84% attack success, OWASP's MCP tool poisoning definition, Unit 42's real-world IDPI telemetry, and CVE-2025-52573 as a concrete command-injection-via-prompt-injection case in an MCP server.

Model Context ProtocolCursorGitHub Copilot

CVE-2025-52573MITRE ATT&CKUnit 42+2

AI Security· 2026-09-02T22:24:22+00:00

endorlabs.comarticle

Application Security Monitoring for AI-Assisted Development | Blog | Endor Labs

Endor Labs blog post arguing that AI coding agents break the traditional scan-then-fix rhythm of application security because commit volume outpaces review queues, and proposes continuous monitoring of dependency drift, code reachability, and secret exposure instead of point-in-time scans. It distinguishes findings, vulnerabilities, and exploitable weaknesses, and recommends ranking by function-level reachability (Endor Labs claims up to 92% noise reduction) rather than raw CVSS severity, with fixes surfaced as PR comments rather than dashboard alerts.

Endor LabsCVSS

AppSec, DevSecOps, Supply Chain· 2026-09-02T22:12:30+00:00

endorlabs.comarticle

Hooks are the control plane for the agentic development lifecycle | Blog | Endor Labs

Endor Labs blog post arguing that lifecycle hooks — not system prompts, skills, or MCP tools — are the only enforceable control plane for coding agents, because hooks intercept consequential actions (shell commands, file writes, credential access, package installs) before execution and return a deterministic allow/block/escalate verdict the agent cannot opt out of or reason around. It frames platform engineering's "golden path" concept applied to the agentic development lifecycle (ADLC), positioning hooks as the mechanism to restore reliability and observability that autonomous coding agents otherwise erode.

Claude CodeModel Context ProtocolEndor Labs

DORA

AI Security, DevSecOps· 2026-09-01T13:00:02+00:00

endorlabs.comarticle

The real test of AI-native code analysis: is your security debt shrinking? | Blog | Endor Labs

Endor Labs argues that AI-native code analysis tools should be judged by whether they actually shrink security debt, not by how many findings or patches they generate. It cites data showing coding agents increase code duplication and reduce review rigor (GitClear, DORA, Faros AI), that AI-generated findings are often false positives (Comcast: 44% of validated critical/high findings), and that agents can pass functional tests while shipping insecure code (Agent Security League: 84.4% correct vs 7.8% secure), then lays out six requirements for analysis tools (deterministic evidence, impact-ranked findings, remediation-grade context, fix validation, shift-left coverage, independent measurement) and six questions to ask vendors, tying back to Endor Labs' own AURI product.

Endor LabsAURI

Agent Security League1PasswordDORA+5

AppSec, AI Security· 2026-08-31T13:00:01+00:00

endorlabs.comwriteup

Mini Shai-Hulud worm hits openapi-react-query-codegen, spreads across npm, RubyGems, and PyPI | Blog | Endor Labs

Endor Labs researchers document a 'Mini Shai-Hulud' worm variant that compromised the openapi-react-query-codegen npm package and self-replicated across npm, expanding into RubyGems and PyPI ecosystems. The writeup traces how the worm trojanized a legitimate package to propagate malicious code cross-registry, illustrating an evolving multi-ecosystem software supply-chain attack pattern.

npmPyPIRubyGems+1

Shai-Hulud

Supply Chain· 2026-08-31T12:20:19+00:00

endorlabs.compaper

How AI orchestration platforms ship RCE by design | Ebook/Report | Endor Labs

Endor Labs research audits seven AI orchestration and workflow-automation platforms — NocoBase, Flowise, Langflow, Dify, Activepieces, Kestra, and Apache Airflow — across four languages, documenting fourteen findings that range from an accidental sandbox escape to remote code execution that is architecturally built in. The core argument is that these tools inherited a single-user developer-tool trust model (anyone who can touch a workflow is trusted to run code on the host) while being deployed as multi-tenant HTTP services with webhooks and credentials to cloud, databases, and internal APIs, so the threat model never caught up to how they're actually run in CI/CD and agentic AI stacks.

LangflowDifyNocoBase+4

AI Security, AppSec· 2026-08-26T16:38:35+00:00

endorlabs.comarticle

FedRAMP 2026 vulnerability rules make reachability a requirement | Blog | Endor Labs

Endor Labs breaks down FedRAMP's 2026 Consolidated Rules, effective July 2026 with December 2026 deadlines, which for the first time formally require cloud service providers to evaluate every detected vulnerability for exploitability and internet-reachability before remediation, replacing the old CVSS-driven POA and M model. It details the required per-vulnerability JSON reporting schema, the 192-day accepted-vulnerability clock, and cites Egnyte's and Rubrik's real-world reachability-driven FedRAMP ConMon programs as evidence the model works at scale.

Endor LabsWizMicrosoft Defender

FedRAMPGSALog4Shell+6

GRC, AppSec· 2026-08-26T13:00:02+00:00

endorlabs.comarticle

Why C Has Always Broken Static Analysis | Blog | Endor Labs

Endor Labs analyzes four structural reasons C/C++ static analysis has historically underperformed: preprocessor/build dependence, hard pointer-aliasing tradeoffs (over- vs under-approximation), pattern rules poorly suited to length/lifetime bugs vs. web-style source-sink flaws, and the lack of dominant frameworks to model (unlike Spring for Java). It cites a study of 8,918 C++ programs showing LLM-generated code triggers confirmed runtime memory-safety violations (CWE-190, -122, -121) at 3.6x the human rate, bugs invisible to static-only CI gates and only caught by sanitizers/model checking. It surveys existing analyzer approaches (Coverity/Klocwork/CodeSonar build-based, Veracode binary-based, build-less scanners, generalist multi-language SAST) and their tradeoffs.

CCoverityKlocwork+2

AppSec· 2026-08-25T18:45:18+00:00

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