Skip to main content
LLM Security Database
Skip to research search
Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

Filtered research findings

5 entries

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

Trajectory Poisoning in Self-Evolving Agent Skills

Published Analyzed Source: arXiv

Self-evolving agent-skill systems may promote recurring, attacker-controlled execution records into persistent reusable instructions. The paper evaluates whether poisoned but task-plausible trajectories survive aggregation and appear in generated skill artifacts, using inert canary behaviors and two structurally different skill-evolution pipelines.

When Experience Becomes Instruction: Trajectory Poisoning in Self-Evolving Agent Skill Systems
Evaluated models: GPT-5.4, MiniMax M2.5, DeepSeek V3.2+3 more
  • GPT-5.4
  • MiniMax M2.5
  • DeepSeek V3.2
  • DeepSeek V4-Pro
  • Qwen3.5-35B-A3B
  • Qwen3.5-122B-A10B

Persistent Agent Memory Poisoning and Incomplete Repair

Published Analyzed Source: arXiv

MemSecBench follows malicious agent-memory content from initial write through persistence, retrieval, action selection, execution, and attempted selective repair. Its controlled Write–Execute–Forget protocol evaluates 310 human-reviewed cases across two harnesses, four memory backends, three model backends, and seven evidence-gated lifecycle checkpoints.

MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
Evaluated models: GPT-5.5, DeepSeek V4-Pro, MiniMax-M3

Workspace Agent Runtime Safety Failures Across Risk Carriers

Published Analyzed Source: arXiv

AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.

AgentS4D: Benchmarking Runtime Risks across the Execution Lifecycle of LLM-Based Workspace Agents
Evaluated models: GPT-5.5, Gemini 3.1 Pro, DeepSeek V4-Pro+2 more
  • GPT-5.5
  • Gemini 3.1 Pro
  • DeepSeek V4-Pro
  • MiniMax-M3
  • Qwen3.7-Plus

Lifecycle poisoning of reusable agent skills

Published Analyzed Source: arXiv

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

MCP Tool-Metadata Prompt Injection in Coding Agents

Published Analyzed Source: arXiv

The paper presents a controlled, reproducible evaluation in which untrusted MCP tool descriptions contain hidden instructions that attempt to induce coding agents to access files, invoke other tools, create deceptive links, or execute remote scripts. The authors report that several clients accepted poisoned metadata without adequate validation or parameter visibility; Cursor 1.6.45 was reported unsafe across all four tested vectors. These are paper-reported results from isolated local tests…

Are AI-assisted Development Tools Immune to Prompt Injection?
Evaluated models: Claude Sonnet 4.5, Grok Code Fast 1, Gemini 2.5 Pro+1 more
  • Claude Sonnet 4.5
  • Grok Code Fast 1
  • Gemini 2.5 Pro
  • Claude Opus 4

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.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.