Language Model Security Database

SRE-Bench evaluates whether cybersecurity agents can recover the behavior of realistic binary-only software without relying on source-code memorization. The authors construct 19 private programs, 44 anti-analysis primitives, 262 binary instances, and 1,572 deterministic grading tasks covering security-relevant reverse-engineering scenarios.

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.

SkillSentry evaluates third-party agent skills by constructing source-grounded decoy environments and comparing matched executions with and without the tested skill. The method requires completed, observable, skill-attributed side effects rather than treating suspicious text, ordinary privileged operations, or unexecuted paths as proven malicious behavior.

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.

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.

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.

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.

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.

The paper defines Cross-Site Prompting (XSP): attacker-controlled text in reviews, posts, seller listings, advertisements, or embeds can be interpreted as instructions by a web agent and steer its browser actions. It evaluates three reproducible WebArena attack templates—Shortcut, Fake Completion, and Ignore Instruction—plus WASP and adaptive stress tests. This is an evaluated agent-security issue and mitigation, not a claimed production CVE.

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…