ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
mSystems, 9(12):e00568–24
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2024 3verdicts
UNVERDICTED 3representative citing papers
GuardAgent safeguards LLM agents by generating task plans from safety requests and mapping them to executable guardrail code, achieving over 98% accuracy on a healthcare access-control benchmark and 83% on a web safety benchmark.
LLMs exhibit persistent inertia in value orientations, with harm avoidance and fairness remaining skewed across persona prompts.
citing papers explorer
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Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
ASB is a new benchmark that tests 10 prompt injection attacks, memory poisoning, a novel Plan-of-Thought backdoor attack, and 11 defenses on LLM agents across 13 models, finding attack success rates up to 84.3% and limited defense effectiveness.
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GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning
GuardAgent safeguards LLM agents by generating task plans from safety requests and mapping them to executable guardrail code, achieving over 98% accuracy on a healthcare access-control benchmark and 83% on a web safety benchmark.
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Inertia in Moral and Value Judgments of Large Language Models
LLMs exhibit persistent inertia in value orientations, with harm avoidance and fairness remaining skewed across persona prompts.