Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
📰 ArXiv cs.AI
Researchers introduce a new threat model for LLM-based web agents, where environmental observations can contaminate memory, enabling persistent attacks across websites and sessions
Action Steps
- Understand the concept of environment-injected memory poisoning attacks
- Recognize how LLM-based web agents' memory storage can be contaminated through environmental observations
- Develop strategies to mitigate these attacks, such as implementing secure memory storage and access controls
- Analyze the potential impact of these attacks on web agent security and develop countermeasures
Who Needs to Know This
Security researchers and AI engineers on a team benefit from understanding this new threat model to develop more robust security measures for LLM-based web agents, as it highlights a previously overlooked vulnerability
Key Insight
💡 Environmental observations can contaminate LLM-based web agents' memory, enabling persistent attacks
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🚨 New threat model: Environment-injected memory poisoning attacks on LLM-based web agents 🚨
Key Takeaways
Researchers introduce a new threat model for LLM-based web agents, where environmental observations can contaminate memory, enabling persistent attacks across websites and sessions
Full Article
Title: Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
Abstract:
arXiv:2604.02623v1 Announce Type: cross Abstract: Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack surface that spans websites and sessions. While existing security research on memory assumes attackers can directly inject into memory storage or exploit shared memory across users, we present a more realistic threat model: contamination through environmental observati
Abstract:
arXiv:2604.02623v1 Announce Type: cross Abstract: Memory makes LLM-based web agents personalized, powerful, yet exploitable. By storing past interactions to personalize future tasks, agents inadvertently create a persistent attack surface that spans websites and sessions. While existing security research on memory assumes attackers can directly inject into memory storage or exploit shared memory across users, we present a more realistic threat model: contamination through environmental observati
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