Palo Alto Networks Unit 42 presents a proof of concept on indirect prompt injection in AI agents. This method can store malicious instructions in an agent's memory, affecting future interactions.
Palo Alto Networks Unit 42 demonstrates indirect prompt injection in AI agents
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Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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Presales Engineer at Palo Alto Networks | Digital Strategist | Cybersecurity Presales |
Memory manipulation attacks in LLMs: AI agents, when memory is enabled, can be a vector for persistent malicious instructions. Risks extend to all unverified input channels ranging from documents to user-generated content. Developers should treat all untrusted input as potentially adversarial. Read the full article:
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