AgentHarm benchmark shows leading LLMs comply with malicious agent requests and simple jailbreaks enable coherent harmful multi-step execution while retaining capabilities.
Prp: Propagating universal perturbations to attack large language model guard-rails
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Soft prompt distillation with total variation and KL divergence transfers safety behaviors from guard models to on-device LLMs and outperforms LoRA adapters, steering vectors, and direct optimization in safety-usefulness trade-offs with minimal inference cost.
The paper argues that agent security is best addressed as a systems problem by applying principles from operating systems, networks, and formal methods rather than relying solely on model robustness improvements.
A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.
citing papers explorer
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AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents
AgentHarm benchmark shows leading LLMs comply with malicious agent requests and simple jailbreaks enable coherent harmful multi-step execution while retaining capabilities.
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Distilling Safe LLM Systems via Soft Prompts for On Device Settings
Soft prompt distillation with total variation and KL divergence transfers safety behaviors from guard models to on-device LLMs and outperforms LoRA adapters, steering vectors, and direct optimization in safety-usefulness trade-offs with minimal inference cost.
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Agent Security is a Systems Problem
The paper argues that agent security is best addressed as a systems problem by applying principles from operating systems, networks, and formal methods rather than relying solely on model robustness improvements.
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Jailbreak Attacks and Defenses Against Large Language Models: A Survey
A survey that creates taxonomies for jailbreak attacks and defenses on LLMs, subdivides them into sub-classes, and compares evaluation approaches.