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.

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.

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.

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.

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.

The MTGuard study evaluates unsafe Model Context Protocol tool calls originating from compromised server data, host-side execution changes, and malicious user-controlled resources. Its hybrid monitor combines pre-execution parameter inspection, behavioral observation, and post-execution result verification across browser-automation and financial-analysis agents.

The paper presents a concrete, reproducible security evaluation in which attacker-controlled instructions embedded in retrieved external content steer stateful, tool-calling LLM agents toward unauthorized actions. It adapts white-box GCG and black-box TAP to AgentDojo and evaluates single-task and task-universal attacks across 80 task pairs in four domains. The reported results show that semantic black-box optimization can discover functional prompt injections more effectively than…

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 black-box jailbreak evaluation in which an evolutionary search generates simulated, role-labeled multi-turn dialogue histories and submits them as a single request. Five semantic mutators and graded judge feedback optimize conversational priming that can induce harmful responses despite direct-request refusals. Results are paper-reported and not independently verified.

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.