FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
AgentSentry: Mitigating indirect prompt injection in LLM agents via temporal causal diagnostics and context pu- rification
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The paper defines causality laundering as an attack leaking information from denial outcomes in LLM tool calls and proposes the Agentic Reference Monitor to block it using denial-aware provenance graphs.
Routine user chats can unintentionally poison the long-term state of personalized LLM agents, causing authorization drift, tool escalation, and unchecked autonomy, as measured by a new benchmark and reduced by the StateGuard defense.
SkillGuard-Robust formulates pre-load auditing of untrusted Agent Skills as a three-way classification task and achieves 97.30% exact match and 98.33% malicious-risk recall on held-out benchmarks.
citing papers explorer
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FlowSteer: Prompt-Only Workflow Steering Exposes Planning-Time Vulnerabilities in Multi-Agent LLM Systems
FlowSteer is a prompt-only attack that biases multi-agent LLM workflow planning to propagate malicious signals, raising success rates by up to 55%, with FlowGuard as an input-side defense reducing it by up to 34%.
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Causality Laundering: Denial-Feedback Leakage in Tool-Calling LLM Agents
The paper defines causality laundering as an attack leaking information from denial outcomes in LLM tool calls and proposes the Agentic Reference Monitor to block it using denial-aware provenance graphs.
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When Routine Chats Turn Toxic: Unintended Long-Term State Poisoning in Personalized Agents
Routine user chats can unintentionally poison the long-term state of personalized LLM agents, causing authorization drift, tool escalation, and unchecked autonomy, as measured by a new benchmark and reduced by the StateGuard defense.
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Structured Security Auditing and Robustness Enhancement for Untrusted Agent Skills
SkillGuard-Robust formulates pre-load auditing of untrusted Agent Skills as a three-way classification task and achieves 97.30% exact match and 98.33% malicious-risk recall on held-out benchmarks.