How I Built an OWASP Memory Guard for AI Agents (ASI06)
Vaishnavi Gudur discusses the development of the OWASP Memory Guard for AI agents, addressing the security risks associated with AI memory usage. The tool aims to prevent memory poisoning attacks by scanning interactions with memory stores. It integrates with various agent frameworks and provides a multi-layer detection pipeline to enhance security.
- ▪The OWASP Agentic AI Top 10 identifies memory poisoning as a significant security risk for AI agents.
- ▪Agent Memory Guard is a Python library designed to scan and protect memory interactions in AI agents.
- ▪The library includes features like pattern matching and semantic analysis to detect potential threats.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3751751) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Vaishnavi Gudur Posted on May 22 How I Built an OWASP Memory Guard for AI Agents (ASI06) #python #ai #security #opensource The Problem: AI Agents Are Trusting Their Own Memory Too Much When you build an AI agent that uses memory — whether it's a vector database, a conversation history store, or a RAG pipeline — you're creating a new attack surface that most security tools completely ignore. The OWASP Agentic AI Top 10 calls this ASI06: Memory Poisoning.
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