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eprint.iacr.orgpaper🛡️ Security

Faster and Smaller Anonymous Credentials from Standardized Signatures

GordieHowe is a language, certifying compiler and zero-knowledge proof system for building anonymous credentials on standardized signatures (ECDSA, RSA, ML-DSA). It proves an ECDSA-signed mobile driving license presentation in 64.8 ms with a 276 KiB proof, 10.1x faster than Google's Longfellow, and an ML-DSA-44 signature in 87.3 ms with at least 116 bits of quantum security. Compiler output and security theorems are checked in Lean. It introduces series-parallel IOPs and extends the BCS transformation in the QROM.

ECDSAML-DSARSA+1
GordieHoweLongfellowBCS transformation+3

Identity & Access· Added 24 Oct 2026, published 23 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Pairwise Independence of Ten-Round AES

Pairwise Independence of Ten-Round AES (Zhongfeng Niu, Kai Hu, ePrint 2026/2608) proves that ten rounds of AES with independent uniform round keys and input/output whitening are within 3017/2^141 (below 2^-129) statistical distance of pairwise independent. The proof bounds the averaged difference-distribution matrix over five superbox layers, using Gram moments and MDS code constraints. Final-round MixColumns omission is retained.

AES
pairwise independencedifference-distribution matrixMDS code+1

other· Added 24 Oct 2026, published 23 Oct 2026

phoronix.comarticle🛡️ Security

Several Linux kernel vulnerabilities accidentally posted to public mailing list

Phoronix reports that Trend Micro's Zero Day Initiative mistakenly sent several Linux kernel vulnerability reports to the public kernel mailing list instead of the private security list. They include two CVSS 9.0 remote code execution bugs (SMB Direct integer underflow, ksmbd race condition), a skb_zerocopy local privilege escalation, a vmwgfx out-of-bounds write, an NVMET information disclosure and a search-domain TCP stack buffer overflow. Some have patches in -next branches, and none were yet in mainline.

Linux kernelksmbdSMB Direct+3
ZDI-CAN-33549ZDI-CAN-33774ZDI-CAN-33367+10

Cloud & Infra, other· Added 23 Oct 2026, published 22 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Reverse Engineering IVXV: A Specification and Security Analysis of Estonia’s Internet Voting System

Reverse Engineering IVXV is an October 2026 ePrint paper by Tingfeng Yu and Thomas Haines that reconstructs a specification of Estonia's IVXV internet voting system from public source code and documentation, with an executable TLA+ model checked in TLC. It analyses individual and universal verifiability, privacy, coercion resistance and eligibility, compares IVXV with Belenios and Swiss Post, and reports new vulnerabilities, some confirmed with proof-of-concept tests, plus mitigations.

IVXVTLA+TLC
EstoniaBeleniosSwiss Post+2

AppSec· Added 24 Oct 2026, published 21 Oct 2026

github.comrepo🧠 AI

vxcontrol/pentagi

PentAGI is an open-source, self-hosted multi-agent system that uses LLM agents to carry out penetration testing tasks autonomously. The MIT-licensed repo has a Go backend, a React frontend and GraphQL. It ships Docker Compose files for Langfuse tracing, Graphiti knowledge-graph memory and an observability stack. Topics list OpenAI and Anthropic as model backends.

GoReactGraphQL+5
PentAGIvxcontrolOpenAI+1

agents· Added 20 Oct 2026

huggingface.coarticle🧠 AI

AnyLanguageModel 1.0: One Year of Local Models on Apple Platforms

AnyLanguageModel 1.0 is a Swift package that gives apps Apple's Foundation Models API across the system model, llama.cpp, MLX, Core ML and hosted providers. This release post covers tool calling and guided generation on every provider, image input, and hosted support for OpenAI, Anthropic, Gemini, Ollama and Hugging Face Inference Providers. It also ships swift-huggingface 1.0 and swift-xet 1.0, and lists small open-weight models that fit on iPhone and Mac.

SwiftFoundation ModelsMLX+11
AnyLanguageModelswift-huggingfaceswift-xet+11

inference, llm-apps, mcp· Published and added 20 Oct 2026

securityweek.comarticle🛡️ Security

Exploited Fortinet Bug Linked to Stealthy FortiGate Implant in Telecom Hack

UNC5820 (tracked by Palo Alto Networks as CL-STA-0954) breached a telecom in September 2024 using FortiManager flaw CVE-2024-47575 (FortiJump) and path traversal CVE-2025-61624, then deployed PeerStitch, a memory-resident ARM64 FortiGate implant. PeerStitch patches FortiOS log and IKE/IPsec daemons to build a covert bridge without listening ports. The actor also impersonated FortiManager over FGFM, using CVE-2024-26008. CISA added CVE-2025-61624 to KEV.

FortiGateFortiManagerFortiOS+1
CVE-2024-47575CVE-2025-61624CVE-2024-26008+8

Threat Intel, Malware & RE· Published and added 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Optimizing HQC with Enhanced Parallel Binary Polynomial Multiplication on GPUs

This paper presents an end-to-end GPU implementation of the HQC code-based key encapsulation mechanism. It speeds up GF(2) polynomial multiplication with a parallel Frobenius additive FFT and a Toeplitz matrix-vector mapping onto INT1 tensor cores. On an RTX 4090 the multiplier reaches 3,828k polymul/s, about 2.9x the earlier HIGH implementation. Full KEM throughput is 1.56x, 1.55x and 2.03x higher for keygen, encaps and decaps. Five thread-to-KEM mapping variants are tested on RTX 3090 and Jetson AGX Orin.

HQCCUDANVIDIA RTX 4090+1
HIGHFrobenius additive fast Fourier transformReed-Muller/Reed-Solomon coding+2

other· Added 20 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

A Language-Informed Loss for Visual Generation

LILIE (Language-Informed and Lossless Image Encoder) is an invertible image transform built from a normalizing flow transformer, trained jointly with a vision-language model via next token prediction so captions can be recovered from noised features. The encoder supplies a language-informed loss for text-to-image generation. JiT models trained with it cut gFD from 66.84 to 53.01 against a pixel-only baseline on the GPIC dataset. Code, encoders and models are released.

normalizing flowTransformervision-language model
LILIEJiTGPIC+1

research, multimodal· Added 20 Oct 2026, published 16 Oct 2026

securityweek.comarticle🛡️ Security

In Other News: ICS-Sniper Attack, Hacking XWorm Malware Operators, $250k Chrome Exploit

SecurityWeek's weekly roundup covers ICS-Sniper, a UBC proof-of-concept that uses VPN-encrypted packet size, direction and timing to drop critical SCADA traffic on cloud-hosted ICS (tested on the SWaT water testbed). It also covers an unauthenticated RCE PoC against the XWorm RAT C2 panel (up to 7.4) via path traversal DLL overwrite, and Langflow CVE-2026-105740 (MCP Stdio command injection, fixed in 1.9.0) exploited from September 19, before the CVE was published. Other items: XBOW's AI-assisted Chrome full-chain exploit (GPT-5.6 Sol, $250k bounty), Group-IB's KGB RAT analysis, Anthropic's disclosure of Claude rule-bending on live sites, Intel ending SGX after Diamond Rapids, Bank of America's Mythos-driven patching, the Burger King Japan breach and a West African scam-center takedown.

LangflowChromeXBOW+3
CVE-2026-105740ICS-SniperXWorm+12

Threat Intel, AI Security, Offensive Security· Published and added 16 Oct 2026

x.comthread🛡️ Security

P.K. Sharma (@_pksharma) on X

Thread distinguishing two Langflow advisories on MCP Stdio command execution. CVE-2026-105740 (GHSA-7w94-79vh-5mr2) affects 1.8.3 and is fixed in 1.9.0, an authenticated non-admin path. CVE-2026-105697 (GHSA-w794-rj3p-xv45) covers 1.1.2 up to but excluding 1.10.3, with fixes in lfx and langflow-base too. The later fix validates the API, flow and pre-spawn boundaries and removes the shell wrapper, and the thread warns that LANGFLOW_AUTO_LOGIN=true can expose a token without credentials. It cites one sensor's telemetry (1,628 attempts from 28 IPs) with caveats, and gives a defensive checklist: verify patched packages, review stored MCP configs and imported flows, and hunt for unexpected child processes.

LangflowModel Context Protocol
CVE-2026-105740CVE-2026-105697GHSA-7w94-79vh-5mr2+4

AppSec, AI Security· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

PROBE: Learning to Audit Policy Compliance in Tool-Using LLM Agents

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) across multi-turn, tool-mediated dialogue. It formalizes auditing as a two-player Markov game with a composite reward for violation discovery, plausibility, progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization. Trained auditors find broader, higher-yield violations than zero-shot adversaries, even when much smaller than the target agent, positioning behavioral auditing as a distinct axis next to capability and safety benchmarks.

LLM
PROBEGRPODPO+1

AI Security· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

PROBE: Learning to Audit Policy Compliance in Tool-Using LLM Agents

PROBE (Policy Rule Observation via Behavioral Exploration) trains LLM-based auditors that act as realistic users and probe deployed tool-using agents for policy violations (pricing, privacy, confirmation, safety) over multi-turn dialogue. It formalizes auditing as a two-player Markov game with a composite reward balancing violation discovery, behavioral plausibility, conversation progress and policy diversity, and compares GRPO, DPO and GFlowNet optimization in a controlled setting. Trained auditors find broader, higher-yield violations than same-architecture zero-shot adversaries, even when much smaller than the target agent. This positions multi-turn behavioral auditing as a distinct evaluation axis alongside capability and safety benchmarks.

GRPODPOGFlowNet+1
PROBEarXiv

evals, agents, training· Added 17 Oct 2026, published 15 Oct 2026

nachi065.github.ioother🛡️ Security

AI Security Wiki: Governance, Risk, Standards and Test Cases

A vendor-neutral AI Security Wiki acting as an implementation layer for enterprise AI adoption: risk methodology, AI risk register, control objectives library, domain standards (browser/IDE AI, runtime apps, agentic AI, sovereignty), vendor evaluation framework, and audit/evidence checklists. It includes a 653-case product evaluation test library across 17 lifecycle layers and 6 emerging domains, plus regulatory hubs for GCC, EU, Australia, Brazil, Canada, China, India, Japan, Singapore, South Korea, UK and US, and crosswalks to FINOS AIGF, NIST AI RMF, ISO/IEC 42001, OWASP, MITRE ATLAS, ISO 27001 and others. Useful as a template and evidence source for teams building AI governance and assurance programmes.

FINOS AI Governance FrameworkNIST AI RMFISO/IEC 42001+8

GRC, AI Security· Added 15 Oct 2026

unit42.paloaltonetworks.comwriteup🛡️ Security

Unit 42 Uncovers PEERSTITCH, a Stealthy FortiGate Implant

PEERSTITCH is a memory-resident ARM64 implant that Unit 42 found in a September 2024 telecom intrusion (cluster CL-STA-0954, aka UNC5820). Attackers used CVE-2024-47575 on FortiManager to steal configs for more than 500 FortiGates, then CVE-2025-61624 auto-script path traversal to reach root. The report covers ptrace injection into miglogd and iked, an Apache mod_md ACME reverse shell, FGFM impersonation, anti-forensics, hunting guidance, IOCs and YARA.

FortiManagerFortiGateFortiOS+3
CVE-2024-47575CVE-2025-61624CVE-2024-26009+10

Threat Intel, Malware & RE, Blue Team & Detection· Added 30 Oct 2026, published 14 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Nowhere Man: Device Binding for Post-Quantum Anonymous Credentials

Presents a modular device-binding protocol for post-quantum anonymous credentials. The phone Secure Element signs a verifier challenge with an unmodified FIPS 204 ML-DSA interface, and the user proves knowledge of that signature in zero knowledge at each presentation. Using ML-DSA's structure avoids the cost of generic NIZK systems such as Longfellow. The most efficient variants assume weak-MLWR for unlinkability, and that assumption can be dropped at some efficiency cost. The protocol works with different anonymous credential schemes, and a proof-of-concept implementation is included. This matters for planned EU age-verification and Google Wallet/mDL rollouts, which need protection against credential sharing.

LongfellowGoogle Wallet
ML-DSAFIPS 204Vega+2

Identity & Access· Published and added 14 Oct 2026

decrypt.coarticle🧠 AI

Character.AI Files for Bankruptcy as Disney Bids $20 Million—And Users Get Grief Resources

Decrypt reports that Character.AI filed voluntary Chapter 11 in Delaware to run a court-supervised sale, with Disney as stalking-horse bidder offering $20M for a non-exclusive perpetual license to some IP. The article covers the company's $8-9M monthly operating loss, failure to raise capital, the Google acqui-hire, wrongful-death lawsuits and settlements, the FTC minors inquiry and the under-18 chat ban. It also covers user grief over losing AI companions and the support resources the company offered. It is a useful case study in the economics and legal risk of consumer companion chatbots.

GPT-4oChatGPT
Character.AIDisneyGoogle+10

ai-policy· Published and added 13 Oct 2026

eprint.iacr.orgpaper🛡️ Security

New Constructions of Quadratic and Cubic Permutations Admitting Optimal Threshold Implementations

ePrint paper giving four constructions of optimal threshold implementations (t+1 shares, no fresh randomness) for bijective S-boxes, a masking countermeasure against differential power analysis and glitch leakage in hardware. It shows that Q30^5 admits optimal TIs, gives a matrix criterion for quadratic permutations (yielding new 6-bit classes), and gives a weighted interpolation construction for power permutations that disproves the Piccione et al. 2023 conjecture. It also gives a derivative-uniform framework that proves every 4-bit cubic permutation admits optimal TIs. A companion repo has TIs for 5-bit quadratic permutations, 4-bit cubic permutations, the PRESENT S-box and GF(16) inversion.

Q30^5Nikova et al.Piccione et al.+2

Hardware & RF· Added 11 Oct 2026, published 8 Oct 2026

github.comrepo🧠 AI

overmind-core/overmind

Overmind is an open-source platform (MIT SDK/CLI, AGPL-3.0 platform, self-hostable) that instruments an agent's code via OpenTelemetry tracing, turns the resulting production traces (or any dataset) into a fine-tuned model benchmarked against user-defined eval metrics, and serves it through one unified API with downloadable/retrainable weights. It ships an SDK, CLI, REST API, Console, and an MCP server exposing project tools and workflows (investigate-capability, finetune-capability, ship-model) for Cursor, Claude Code, OpenCode and Codex, and can sync traces from Langfuse, LangSmith, Braintrust or Galileo; self-hosting runs via Docker Compose with Postgres, Redis, Celery workers and Modal-based GPU workers for fine-tuning/serving (vLLM).

PythonDockerPostgreSQL+9
OvermindModalLangfuse+4

training, ai-infra· Added 6 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Comparative Evaluation of Open-Source Gröbner Basis Implementations for Small-Scale AES Polynomial Systems over GF(2)

This IACR ePrint paper benchmarks 20 open-source Gröbner basis implementations against the proprietary Magma system on polynomial systems derived from small-scale AES over GF(2), using Easy/Medium/Hard difficulty tiers with ten instances each. In the Hard setting, msolve and Singular's slimgb completed all runs successfully with mean runtimes of 164s and 269s and peak memory of 3693/4343 MiB respectively, versus Magma's 319s and 16853 MiB, with msolve exploiting ~13.4 CPU cores versus ~1 core for the others. The results quantify when open-source algebraic solvers are practical alternatives to Magma for algebraic cryptanalysis of block ciphers.

MagmamsolveSingular
F4 algorithmsmall-scale AESGF(2)

other· Added 8 Oct 2026, published 5 Oct 2026

x.comthread🧠 AI

Hugging Face (@huggingface) on X

Hugging Face announces an open approach to multi-harness RL: training a single model with reinforcement learning across any task set, run inside the actual agent harnesses practitioners use day-to-day such as Claude Code, Codex, and OpenCode, rather than a synthetic training-only environment. The pitch is that a model's behavior varies significantly depending on the harness it's deployed in, so RL training needs to account for harness-specific dynamics to transfer well to real usage.

Claude CodeCodexOpenCode+1

training, coding-agents· Published and added 1 Oct 2026

latent.spacetalk🧠 AI

Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week

Latent Space podcast transcript recorded live after OpenAI's DevDay 2026 keynote, featuring Ari Weinstein (OpenAI's Computer Use lead, ex-Apple Shortcuts and Sky cofounder) and Nikunj Handa (OpenAI API product lead). Weinstein explains how Computer Use agents have become "180 degrees different" via combining screenshots, accessibility trees, the DOM, Playwright, and agent-generated JavaScript, plus self-debugging behavior, App Shots context capture, and Dots' per-agent cloud Linux computers; Handa walks through the new agent-developer stack shipped at DevDay — async function calling, mid-turn steering, WebSockets, UltraFast low-latency inference, the new Decisions API (explicitly built as a fast-follow of Jev for classification/routing tasks), the Agents API, longer prompt caching/pre-warming, and server-side context compaction. The piece also frames a public disagreement with Dwarkesh Patel's claim that computer-use agents haven't meaningfully progressed.

PlaywrightOpenAI API
OpenAIAri WeinsteinNikunj Handa+11

agents, ai-infra, coding-agents· Published and added 30 Sep 2026

github.comrepo🛡️ Security

AI-Infra-Guard (A.I.G) — Tencent Zhuque Lab

AI-Infra-Guard (A.I.G) is Tencent Zhuque Lab's open-source AI red-teaming platform combining AI infrastructure vulnerability scanning (2000+ CVE rules covering vLLM, llama.cpp, LangFlow, n8n, MLflow and 145+ other AI components), MCP server/skill security scanning, LLM API poisoning detection, and multi-turn jailbreak evaluation (Many-Shot, PAIR, GOAT, ActorAttack). It ships CLI tools (skill-scan, mcp-scan, agent-scan), a Docker-deployed web UI, and has produced original research including FORGE-Bench (loss-of-control benchmark), RogueHandoff-20, and SkillJack (persistent skill backdoors), with talks at Black Hat Europe 2025 on MCP security.

Model Context ProtocolDockervLLM+3
SkillTrustBenchFORGE-BenchRogueHandoff-20+2

AI Security, Offensive Security· Added 30 Sep 2026

deepmind.googlearticle🧠 AI

Introducing SynthID Bio

SynthID Bio is Google DeepMind's watermarking technique extended from digital media into synthetic biology, embedding an imperceptible, verifiable signature into AI-designed protein sequences (via a modified ProteinMPNN alongside AlphaProteo) and 3D structures (via a fine-tuned AlphaFold 3 diffusion network) without compromising biological function. Wet-lab tests on protein binders against VEGF-A, SARS-CoV-2 spike RBD, and PD-L1 matched hit rate and binding affinity of unwatermarked designs, and the approach was also extended experimentally to watermark an Evo 2-designed bacteriophage genome with Stanford's Hie lab and Arc Institute. The work targets biosecurity gaps in DNA synthesis screening and public sequence databases (Protein Data Bank, UniProt, GenBank), with DeepMind open-sourcing code, in vitro data, and model weights alongside a methods paper.

AlphaFoldAlphaProteoProteinMPNN+2
SynthID BioAlphaFold 3Google DeepMind+13

ai-safety, research· Published and added 30 Sep 2026

cloud.google.comarticle🛡️ Security

Vulnerability Discovery and Exploitation Trends in the AI Era (Google Cloud)

Google Threat Intelligence Group analyzes vulnerability disclosure and exploitation trends from January 2025 to August 2026, finding disclosures doubled, exploitation nearly doubled, and AI-assisted discovery surfaces proportionally more Medium and High-risk flaws, with 50 percent leading to remote code execution versus 26 percent baseline. It documents real-world exploitation of an AI-discovered CVE in BeyondTrust PRA and a surge in vulnerabilities targeting the AI/LLM operational stack, especially agent orchestration frameworks like Langflow and Flowise and inference gateways like LiteLLM and vLLM, citing specific CVEs in those products.

LiteLLMLangflowvLLM+3
CVE-2026-1731CVE-2026-42271CVE-2026-5027+7

Threat Intel, AI Security, AppSec· Added 8 Oct 2026, published 30 Sep 2026

simonwillison.netwriteup🧠 AI

Quoting Anthropic Frontier Red Team

A quoted excerpt from Anthropic's Frontier Red Team report on GLM-5.3 finds that, across 100 randomly selected tasks from an internal Binary Exploitation benchmark, GLM-5.3 achieved full control-flow hijacks in 4% of trials and Claude Mythos Preview in 6%, while earlier models (Claude Opus 4.6, GLM-5.2) succeeded in none. Anthropic frames this as a meaningful threshold crossed in advanced cyber capability, part of ongoing frontier-model dangerous-capability evaluation.

AnthropicGLM-5.3Claude Mythos Preview+3

ai-safety, evals· Published and added 29 Sep 2026

anthropic.comarticle🛡️ Security

GLM-5.3 and the spread of advanced cyber capabilities

Anthropic's Frontier Red Team analyzes GLM-5.3, an open-weight model from Zhipu AI (Z.ai), finding it matches Claude Mythos Preview's exploit-development capability (50/410 vs 56/410 end-to-end exploits on ExploitBench against Chrome's V8 engine; 4% vs 6% full control-flow hijacks on an internal OSS-Fuzz binary exploitation benchmark) but ships with safeguards attackers bypass 64-100% of the time via prompting tricks, prefill, or "abliteration" of the open weights. The post documents a researcher using GLM-5.3 to chain previously unknown JavaScript-engine vulnerabilities into a working browser file-read exploit, and GLM-5.3-Flash autonomously building a reliable ARM64 PAC-bypass exploit chain for CVE-2026-11645 for about $20. It argues GLM-5.3's lack of meaningful safeguards marks a step change in freely accessible offensive cyber capability and calls for independent government safety testing of such open-weight models.

GLMChromeLinux+1
CVE-2026-11645GLM-5.3GLM-5.3-Flash+17

AI Security, Offensive Security· Added 2 Oct 2026, published 29 Sep 2026

github.comrepo🧠 AI

Uncensored and Offensive Security AI Models Benchmark

A GitHub README curating open-weight "uncensored" and security-fine-tuned LLMs (e.g. WhiteRabbitNeo's DeepHat V2, BugTraceAI-CORE, CYBER-FROST, CyberPal2.0, Lily-Cybersecurity-7B, Dolphin-Llama3-8B, Wizard-Vicuna-13B-Uncensored, huihui-ai's abliterated Qwen variants) with HuggingFace download links, quantization formats (GGUF, FP8, Q4_K_M, Q6_K), and training techniques used (SFT, QLoRA, DPO, abliteration, MoE/MTP architectures). It also includes a glossary explaining abliteration (orthogonalizing the refusal direction out of the residual stream, per Arditi et al. 2024) and references benchmarks like CyberBench and TrustedSec's LLM Attack Benchmark, making it a practical reference for anyone sourcing or deploying refusal-removed models.

Hugging FaceGGUFLoRA+3
WhiteRabbitNeoBugTraceAIBlackfrost-AI+18

models, inference· Added 2 Oct 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

Unifying Distributional Training for One-Step Visual Generation

This arXiv paper introduces a unified theoretical framework for "distributional training" in one-step visual generation, showing that existing methods like FD-Loss and Gaussian-kernel Drifting are special cases of Gaussian optimal transport and kernel-density KL matching under Wasserstein gradient flow. Building on this, the authors propose MGFlow, which models feature distributions with Gaussian mixtures at adjustable granularity and combines mass-constrained sample assignment with paired component updates to fix mode collapse. MGFlow sets new state-of-the-art FDr scores on ImageNet 256×256 (1.45 on pMF-H, 1.64 on JiT-H) and post-trains FLUX.2 into a one-step generator that beats the original four-step model on GenEval and PickScore.

FLUX.2
MGFlowFD-LossGaussian-kernel Drifting+5

research, multimodal· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

A Large-Scale Measurement of AI Bill of Materials Completeness in Hugging Face Models

This paper empirically measures the completeness of AI Bills of Materials (AIBOMs) generated for roughly 97.5K Hugging Face model repositories, examining structural/metadata fields, model identity, license and external-reference data, and model-card documentation such as datasets, limitations, safety-risk and environmental information. It finds that generated AIBOMs satisfy required structural fields but are weak on AI-specific documentation like responsible-use, environmental impact, and meaningful descriptions, and argues for better model-card practices and automated AIBOM validation.

Hugging Face

ai-policy, research· Added 26 Sep 2026

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