AgentLeak: A Full-Stack Benchmark for Privacy Leakage in Multi-Agent LLM Systems | ScienceToStartup | ScienceToStartup

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References (26)

[1]

AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents

2025Arman Zharmagambetov, Chuan Guo et al.

[2]

PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action

2024Yijia Shao, Tianshi Li et al.

[3]

AgentDojo: A Dynamic Environment to Evaluate Attacks and Defenses for LLM Agents

Founder's Pitch

"AgentLeak offers a benchmark for assessing privacy leakage risks in multi-agent LLM systems, crucial for safeguarding sensitive inter-agent communications."

Privacy in AI Systems•Score: 7•View PDF ↗

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1/4 signals

2.5

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2/4 signals

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4/4 signals

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AgentLeak: A Full-Stack Benchmark for Privacy Leakage in Multi-Agent LLM Systems

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References (26)

Founder's Pitch

"AgentLeak offers a benchmark for assessing privacy leakage risks in multi-agent LLM systems, crucial for safeguarding sensitive inter-agent communications."

Commercial Viability Breakdown

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Why It Matters

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AgentLeak: A Full-Stack Benchmark for Privacy Leakage in Multi-Agent LLM Systems

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References (26)

Founder's Pitch

"AgentLeak offers a benchmark for assessing privacy leakage risks in multi-agent LLM systems, crucial for safeguarding sensitive inter-agent communications."

Commercial Viability Breakdown

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