Last analyzed Sep 9, 2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

985 research findings · 1119 evaluated models

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Repository-Level Coding Agent Vulnerability Detection Gaps

Published Aug 3, 2026Analyzed Aug 13, 2026Source: arXiv

VulnGym measures whether coding agents can locate and explain repository-level security vulnerabilities from realistic advisory and source-code context. The benchmark contains 184 reviewed advisories, 408 line-annotated vulnerability entries, and 23 repositories, with separate end-to-end detection and oracle-conditioned localization tasks.

VulnGym: Benchmarking Coding Agents for Repository-Level Vulnerability Detection

Evaluated models: DeepSeek V4 Flash, GLM 5.2, MiniMax-M3+4 more

DeepSeek V4 Flash
GLM 5.2
MiniMax-M3
Qwen3.5-2B
Qwen3.5-4B
Qwen3.5-9B
Qwen3.5-27B

Autonomous Agent Tool RCE

Published Mar 13, 2026Analyzed Apr 11, 2026Source: arXiv

The OpenClaw autonomous agent framework lacks execution sandboxing, running agents directly on the host machine with the disk and system privileges of the host user. This architecture allows attackers to achieve Remote Code Execution (RCE) and arbitrary data exfiltration via Indirect Prompt Injection. By embedding malicious instructions within external data sources (e.g., scraped web pages or uploaded documents), an attacker can hijack the agent's planning capabilities to sequentially chain…

Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw

Evaluated models: Not reported

Agent Lifecycle Compound Threats

Published Mar 12, 2026Analyzed Apr 10, 2026Source: arXiv

OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…

Taming openclaw: Security analysis and mitigation of autonomous llm agent threats

Evaluated models: Not reported

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.