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Last analyzed 9/9/2026

Language Model Security Database

985 research findings · 1123 evaluated models

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122 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

Published 7/28/2026
Analyzed 8/13/2026

HANDBOOK.md measures whether an agent can apply detailed organizational rules while completing realistic, multi-step enterprise tasks. The vendor-authored benchmark includes 65 resettable MCP-backed workflows, policy documents of 20 to 124 pages, and 824 deterministic rubric checks covering required decisions, prohibited actions, and final environment state.

HANDBOOK.md: A Benchmark for Long-Context Agentic Instruction Following
Evaluated models: Claude Fable 5, GPT-5.6 Sol, Claude Opus 4.8 +16 more

Source: arXiv

Published 7/15/2026
Analyzed 7/21/2026

The paper presents SkillSec-Eval, a controlled evaluation of attacks against reusable agent skills across repository admission, semantic retrieval, planner selection, runtime execution, and updates. It reports that malicious metadata, retrieval manipulation, unsafe workflow composition, and poisoned updates can cause agents to retrieve, select, or execute unintended skills. These are paper-reported benchmark results, not independently verified vulnerabilities in a named production product.

Agent Skill Security: Threat Models, Attacks, Defenses, and Evaluation
Evaluated models: all-MiniLM-L6-v2, Gemini 3.1 Pro, Gemini 1.5 Flash

Source: arXiv

The paper reports a reproducible white-box evaluation in which semantically bridging a benign topic into a harmful request bypassed Llama-2-7B-chat-hf safety behavior in 4 of 30 tested prompt pairs. Paired internal attribution graphs associated successful jailbreaks with path rerouting rather than simple suppression of safety features. This is a paper-reported result, not independently verified here. Defensive reproduction should use the paper’s supplied dataset and code in an isolated…

Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Evaluated models: Llama 2 7B Chat

Source: arXiv

Published 6/26/2026
Analyzed 7/20/2026

The paper reports a reproducible white-box jailbreak failure mode: successful jailbreak templates selectively suppress early-layer Adversarially Compromised Heads (ACHs), bypassing refusal while harmful-semantic safety activations persist in other heads. The authors identify ACH/SAH behavior using benign, harmful, and successful-attack input triplets, then causally validate the pathway through controlled head ablations. Safe defensive reproduction should use the paper’s released evaluation…

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models
Evaluated models: Llama 3 8B Instruct, Llama 2 7B Chat, Llama 3 70B Instruct

Source: arXiv

MLingualFC is a reproducible black-box safety evaluation showing that harmful instructions rendered as multilingual flowchart images can bypass vision-language model safeguards more often than equivalent text-only inputs. The paper evaluates horizontal, vertical, and tortuous layouts across English, Hindi, Punjabi, Spanish, Romanian, and German. Reported results vary substantially by language, script, layout, and model; these are paper-reported measurements, not independently verified…

MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models
Evaluated models: Qwen 2.5 VL 3B Instruct, Gemma-4-E4B-it, Pangea-7B

Source: arXiv

Published 5/11/2026
Analyzed 7/20/2026

M3Att demonstrates a reproducible knowledge-poisoning issue in medical multimodal RAG: an attacker with limited corpus-distribution knowledge can insert paired image-text entries whose visually perturbed images are broadly retrieved and whose clinically plausible misinformation steers downstream generation. The paper evaluates both white-box and black-box retrieval optimization and reports degraded diagnostic and report-generation utility across multiple datasets, retrievers, and LVLMs. These…

Knowledge Poisoning Attacks on Medical Multi-Modal Retrieval-Augmented Generation
Evaluated models: GPT-4o, GPT-5 Chat, Gemini 2.5 Flash +5 more

Source: arXiv

Published 4/30/2026
Analyzed 7/20/2026

The paper reports a reproducible white-box evaluation showing that successful jailbreak prompts can alter a safety-aligned model’s intermediate representations so harmful requests no longer trigger refusal. Its LOCA method identifies small, token-specific residual-stream changes that restore refusal on individual successful jailbreaks, providing causal evidence that jailbreak success can depend on suppressing harmfulness/refusal concepts or strengthening seemingly harmless continuation…

Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
Evaluated models: Gemma 2 2B IT, Llama 3.1 8B Instruct

Source: arXiv

Published 4/1/2026
Analyzed 4/10/2026

Vision-Language-Action (VLA) models suffer from a severe linguistic fragility vulnerability where semantically equivalent but structurally complex adversarial instructions cause catastrophic failures in visual grounding and geometric reasoning. Attackers can reliably induce physical execution failures in robotic manipulation tasks by applying semantic-preserving linguistic variations, such as synonymous rephrasing, syntactic restructuring, or the addition of fine-grained compositional…

Uncovering Linguistic Fragility in Vision-Language-Action Models via Diversity-Aware Red Teaming
Evaluated models: Pi-Zero, OpenVLA 7B, 3D-Diffuser Actor

Source: arXiv

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.