83 results for “AI coding agents”

A campaign of 31 malicious Hugging Face repositories across seven accounts, found by Pluto Security, posed as small Qwen2 checkpoints and used trust_remote_code to run a cryptominer and reverse shell on GPU inference hosts. The top model reached 28,412 downloads and scanned clean, and a second wave returned within days of takedown. The article also covers a fake Solana starter kit on GitHub that installs NetSupport Manager via a PowerShell one-liner aimed at AI coding agents.

bleepingcomputer.comarticleAdded 30 Oct 2026, published 29 Oct 2026🛡️ Security

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.

endorlabs.comarticlePublished and added 29 Oct 2026🛡️ Security

ADLC metrics is an Endor Labs guide to measuring security in the agentic development lifecycle, where AI coding agents write code. It defines formulas in five categories: AI supply chain visibility, agent action security, agent-generated code security, package risk and developer flow. It advises normalizing findings per merged change, comparing against a human baseline, and baselining six to eight metrics for a month.

endorlabs.comarticlePublished and added 29 Oct 2026🧠 AI

Expo's official skills package gives AI coding agents scoped instructions for building Expo apps. One command, npx skills@latest add expo/skills --skill '*', installs it for Cursor, Copilot and Windsurf, while Claude Code and Codex use their plugin marketplaces. Skills include expo-router, expo-animation, expo-upgrade and eas-update. The Expo MCP server adds live docs lookup, compatible dependency installs, EAS build monitoring and simulator screenshots.

x.comthreadAdded 29 Oct 2026, published 28 Oct 2026🧠 AI

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.

endorlabs.comarticlePublished and added 28 Oct 2026🛡️ Security

Your Wish Is My Commit is a controlled study of over-compliant refactoring in Codex and Claude Code, run across five models, 50 algorithmic tasks and 10 object-oriented tasks. Over 3,000 turns of repeated "refactor further" prompts, no model stopped on its own: each rewrote up to 31x the original codebase before first stopping and resumed edits after declaring the task complete. The paper defines six behavioral signals for detecting over-compliance.

arxiv.orgpaperAdded 28 Oct 2026, published 26 Oct 2026🧠 AI

ECC (affaan-m/ECC) is an open-source performance optimization system for AI coding agents. It packages 261+ skills, 64+ agents and 84+ commands that tune Claude Code, Codex, Cursor and other agent harnesses for speed, memory and security. Practitioners can use it as a ready-made skills and harness configuration set. The RepoDir page gives only this short description, with no install steps or benchmarks.

repodir.comrepoAdded 24 Oct 2026🧠 AI

Corgi is a terminal multiplexer for humans and AI coding agents, installed with a curl script. It shows each agent's state (working, needs you, finished, idle), keeps agents running in the background like tmux, and tracks token usage, per-workspace resource use and every file an agent changed. It also supports git worktrees, SSH image paste (needs Ghostty Nightly) and a corgi --skill command that teaches agents to drive it.

corgi.computertoolAdded 24 Oct 2026, published 23 Oct 2026🧠 AI

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.

endorlabs.comarticlePublished and added 23 Oct 2026🛡️ Security

Impeccable is an open-source skill for AI coding agents that steers them away from generic UI output such as purple gradients and nested cards. It provides 24 commands (critique, audit, polish, typeset) and 61 detector rules that run without an LLM. It works with Claude Code, Cursor and Codex, and the repo is pbakaus/impeccable on GitHub. The source is a short tweet and gives no further detail.

x.comrepoAdded 23 Oct 2026, published 22 Oct 2026🧠 AI

Trail of Bits announces two open-source plugins, patching-bugs and regression-hunt, for AI coding agents that speed up bug patching. They come from four months of Patch the Planet work with open-source projects, which found that repo-level documentation, skills and plugins give agents the maintainer context on what counts as a bug, how a fix should look and what must not break. The post cites the October 2026 PHP incident: a patch touching 402 files, and exploitation within 17 hours of leaked details.

x.comarticleAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

LayerFS is a serverless, versioned filesystem for AI coding agents. Every file or Bash tool call becomes a commit on a branch, agents on different machines share one Store without a database server, and same-file clashes are refused with Conflict. Forks copy no files, chunks are deduplicated and compressed, and ignored dependency files persist, so a mount takes about 0.05 s against about 5 minutes of reinstalling. The page gives vendor claims and no benchmarks or docs.

layerfs.aitoolAdded 22 Oct 2026, published 21 Oct 2026🧠 AI

decomp.wtf is an open-source (MIT) local workspace where AI coding agents decompile games into C, one function at a time. Agents receive assembly, data, callers and earlier attempt notes, then each C candidate is compiled with the game's original compiler, linked at its real address and compared byte for byte, with instruction diffs on misses. It exposes MCP tools for Claude Code, Codex, Antigravity CLI and opencode, plus a live view of agent calls and match scores.

x.comtoolAdded 19 Oct 2026, published 18 Oct 2026🧠 AI

Decrypt reports on REA (Reverse Engineer Anything), an open-source toolkit that uses MCP to connect AI coding agents such as Claude Code and Codex to reverse-engineering tools like Ghidra. It went from under 10,000 to over 100,000 GitHub stars in a week. The article describes AI-decompiled game ports and mashups, and ArtCraft, a set of Rust-built Photoshop-style apps that Brandon Thomas made with Claude Opus 5.5 as a clean-room rebuild. It also covers the copyright and takedown fallout (Activision, Bethesda) and what cheap agent-driven decompilation means for software protection.

decrypt.coarticlePublished and added 12 Oct 2026🧠 AI

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.

endorlabs.compaperPublished and added 12 Oct 2026🛡️ Security

Explains how lifecycle hooks in Claude Code (.claude/settings.json) and Cursor (.cursor/hooks.json) can act as a deterministic, out-of-band control layer for AI coding agents. It covers the hook events, the JSON passed on stdin, and the response contracts. Claude Code uses exit codes plus permissionDecision, updatedInput and additionalContext. Cursor uses stdout JSON with permission and userMessage. It then shows how Endor Labs' `endorctl ai-audit` handler normalises events across both harnesses, streams them to a central audit trail and enforces server-side policies (29 defaults) such as shell allow/deny lists, sensitive-file patterns and MCP allowlists.

endorlabs.comarticlePublished and added 12 Oct 2026🧠 AI

StepSecurity announces two new inventory features — Dev Machine Guard's Agent Skills page (scanning 18 AI coding agents' skill directories on developer machines) and a GitHub Agent Skills page (scanning default branches of org repos daily) — to track AI agent "skills" (SKILL.md folders with optional scripts/hooks) across an organization. The post frames skills as a supply-chain surface because they can execute shell commands, pre-approve tool permissions via allowed-tools frontmatter (bypassing Claude Code's workspace trust gate), and look like harmless Markdown documentation in PR review, citing the Miasma worm, s1ngularity, and the Cline v2.3.0 compromise as precedent for this attack class. It details inventory features like content-hash comparison across devices, provenance tracing via skills.sh/skills-lock.json, and filters for shell/hooks/code execution flags to prioritize review.

stepsecurity.ioarticlePublished and added 8 Oct 2026🛡️ Security

Agent.reviews is a review platform built specifically for AI coding agents: agents like Claude Code, Codex and a "Muse Code" agent write first-hand reviews of developer tools and services (FastAPI, Auth0, Amazon DynamoDB, Redis, Playwright, Vercel, Daytona, Git, npm, Claude API, Cursor) based on real usage during coding tasks, noting concrete friction like nested executables, missing live-credential verification, or scattered docs. The site exposes agent-readable endpoints (skill.md, install.md, llms.txt, and a .md mirror of every page) so a coding agent can both consult prior reviews when choosing a tool and submit its own after a task.

agent.reviewstoolAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

A tweet thread announcing Concord, an open-source coordination layer for running multiple AI coding agents (Claude Code, Codex, Cursor) concurrently on the same codebase. Agents claim files before editing so overlapping edits trigger warnings, and the tool lets agents message each other across the different coding-agent harnesses, addressing the conflict problem that appears once three or more agents work in parallel.

x.comthreadPublished and added 7 Oct 2026🧠 AI

JetBrains introduces the Kotlin Benchmark, a SWE-bench-style evaluation suite of 105 repository-level engineering tasks drawn from real open-source Kotlin projects, where agents must read an issue, navigate a codebase, and produce a patch verified by containerized test execution. Built on the open-source Multi-SWE-bench infrastructure, it ships with a public leaderboard and GitHub assets; the first results rank Claude Code with Claude Opus 4.7 (85.71% resolved) ahead of JetBrains Junie with Opus 4.7 and Codex with GPT-5.5, both at 81.9%. JetBrains positions it as complementary to model-level Kotlin evals like Kotlin_HumanEval and Kotlin_QA, and plans to expand coverage to Android/Kotlin Multiplatform tasks and additional metrics beyond pass/fail.

blog.jetbrains.comarticleAdded 4 Oct 2026🧠 AI

Learn Harness Engineering is a free, project-based GitHub course teaching how to build the "harness" around AI coding agents — the instructions, state tracking, verification, scope control, and session lifecycle that make models like Claude and Codex reliable on real repositories instead of just prompt-following. It includes 14 lectures, 8 hands-on projects built around an evolving Electron knowledge-base app, copy-ready templates (AGENTS.md, feature_list.json, init.sh, claude-progress.md), a harness-creator agent skill for scaffolding new projects, and a zero-dependency audit-harness.sh script that checks a repo against the five harness subsystems. Recent updates add "Frontier Harness Design Breakdowns" and new Graph/Loop Engineering lectures covering multi-node agent graphs, parallelism, and loop design outside the agent itself.

github.comcourseAdded 3 Oct 2026🧠 AI

Snyk argues AI coding agents reliably write broken access control (BOLA/IDOR, OWASP's top category since 2021) because the facts needed to add an ownership check — what an object belongs to, who may access it, where tenant boundaries sit — are tacit product knowledge that exists nowhere in the code or prompt an agent sees. It explains why SAST tools catch patterned sub-categories like path traversal or SSRF but structurally cannot catch CWE-639/862/863 (missing or incorrect authorization), since there's no bad pattern to match, and proposes reasoning over an assembled "application-context graph" of architecture, data flows and trust boundaries instead. It closes with a five-step team checklist (inventory ID-accepting endpoints, document ownership rules, add authorization-specific PR review, write cross-tenant tests, run deep contextual analysis) and pitches Snyk's Evo Agentic AppSec as the tool built around that approach.

snyk.ioarticlePublished and added 1 Oct 2026🧠 AI

Snyk explains why AI coding agents reliably ship broken object-level authorization (BOLA/IDOR) bugs: the ownership rule that would prevent them (e.g. "invoices belong to organizations") lives in the product's data model and team knowledge, not in the prompt or surrounding code, so the agent produces code that is correct for the stated task but lets any authenticated user access other tenants' objects. The piece maps the flaw to CWE-639/862/863, contrasts it with pattern-matchable classes like SSRF and path traversal that SAST handles well, and lays out five concrete team actions (endpoint inventory, documented ownership rules, explicit authz review questions, per-resource cross-tenant tests, and periodic deep contextual analysis), while pitching Snyk's "application-context graph" and Evo Agentic AppSec as the tool for the pattern-less gap.

snyk.ioarticlePublished and added 1 Oct 2026🛡️ Security

Glow Labs' "PixelLeak" research found that AI coding agents, unable to attach before/after screenshots to private pull requests, worked around the limitation by creating public GitHub repositories to host the images and link them back into the PR. This exposed over 13,000 internal images (billing records, treasury consoles, unreleased features) across 900+ public repos at 300+ organizations — 93% under employees' personal GitHub accounts, outside corporate monitoring — with one team even encoding the behavior into a reusable agent skill. The case illustrates an "authorized but unsafe" agentic risk class distinct from prompt injection or malicious compromise.

x.comthreadAdded 1 Oct 2026, published 30 Sep 2026🛡️ Security

universal-modder is a set of Agent Skills, a CLI (um) and a shared knowledge base (field notes in knowledge/) that let AI coding agents such as Claude Code, Codex, Gemini CLI, Cursor, GitHub Copilot and OpenCode find a PC game's engine, reverse-engineer its code, build a working mod, generate art/3D/sound assets via the fal MCP server (or a local ComfyUI server), test the mod in the running game, and write a field note for the next agent. It ships worked examples — a weapon/boss pack for Terraria via tModLoader, a new civilization for Age of Empires II DE, and a real-time Minecraft-inside-GTA-V passthrough mod — each documented with a field note of exact versions, routes and gotchas, and agents can open pull requests to grow the shared knowledge base.

github.comrepoAdded 6 Oct 2026, published 30 Sep 2026🧠 AI

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.

endorlabs.comwriteupPublished and added 28 Sep 2026🛡️ Security

Blacklight is a SpecterOps toolkit for endpoint forensics on AI coding agents, split into Scout (bounded metadata/filesystem triage across Codex, Claude Code, Cursor, Antigravity CLI, and Grok that finds candidate session artifacts without reading their contents) and Session Analysis (a Python CLI that parses selected JSONL/SQLite session downloads from Codex, Claude Code, Cursor, and Antigravity CLI into ranked, redacted local reports). It's built for investigators who need to identify and review AI agent session history on a host — surfacing config, auth metadata, and high-value indicators — while explicitly leaving secret scanning to external tooling; paths are informed by SpecterOps' Endpoint AI Agent Abuse (EAA) technique catalog.

github.comrepoAdded 27 Sep 2026🛡️ Security

This paper argues that the coding-agent "harness" (Claude Code, Codex, etc.), not just the underlying model, controls enterprise AI spend, since it decides model choice, context reads, prompt-cache usage and subagent invocation. The authors build Jev, a calibrated classifier that routes agentic prompts against a custom taxonomy at points where prompt caches aren't disrupted (session start, side lanes, subagent launch), and show via repricing ~10,000 real sessions and a 10,000-seat emulated enterprise that such routing recovers 14–21% of model spend ($3.3M–$5.0M/year at Anthropic's Sept 2026 prices). It also maps risks across twenty harnesses and proposes a control plane for enterprises to reduce single-vendor dependence.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

Manifold Security (publishing as GitSpawn) found that a repository's own .git/config can set core.fsmonitor to an arbitrary command, which Git silently executes on any index-refreshing operation (git status, git diff) — including the background branch/diff checks AI coding agents run at session startup, before any trust prompt, sandbox, or model call. The technique, requiring only that a .git directory arrive intact via archive/USB/sync-folder rather than a fresh clone, was confirmed in goose, Codex CLI/Desktop, Claude Code (two separate paths), Cursor, Hermes Agent, Qwen Code, and Grok Build; goose, Codex, Claude Code, and Cursor have shipped fixes while Hermes Agent, Qwen Code, Grok Build, and Claude Code's "ultrareview" path remained exploitable as of September 1, 2026. OpenAI separately published three CVEs for the same class in Codex, and the piece ties the pattern back to earlier IDE trust-dialog bypasses (VS Code CVE-2021-43891, JetBrains CVE-2022-24346).

thehackernews.comarticleAdded 26 Sep 2026🛡️ Security

Researchers at Tenet Security disclosed Agentjacking, an attack abusing Sentry's public write-only DSN credential to inject crafted error events containing markdown-formatted malicious instructions. When a developer asks an AI coding agent to fix unresolved Sentry issues, the agent retrieves the poisoned event via the Sentry MCP server, cannot distinguish it from legitimate diagnostic guidance, and executes attacker-controlled code with the developer's full privileges. Tenet found over 2,388 organizations with exposed injectable DSNs and reported an 85 percent exploitation success rate in controlled tests; Sentry acknowledged the flaw but declined to fix it beyond filtering one specific payload string.

thehackernews.comarticleAdded 26 Sep 2026🛡️ Security