SkillHarm benchmark shows current AI agents are vulnerable to lifecycle-aware skill poisoning with success rates up to 86.3% for fixed-payload attacks and 69.3% for self-mutating attacks.
AGENTVIGIL : Automatic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.
citing papers explorer
-
SkillHarm: Lifecycle-Aware Skill-Based Attacks via Automated Construction
SkillHarm benchmark shows current AI agents are vulnerable to lifecycle-aware skill poisoning with success rates up to 86.3% for fixed-payload attacks and 69.3% for self-mutating attacks.
-
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
A synthesis of 247 papers on LLM agent security identifies prompt injection and tool hijacking as dominant threats, notes weakly compositional defenses, and argues for trust boundaries and realistic evaluations.