Researchers at Google DeepMind introduced CaMeL, a security layer designed to combat prompt injection attacks targeting large language models (LLMs). By utilizing traditional software security principles such as control flow integrity and access control, CaMeL can neutralize 67% of attacks based on findings from the AgentDojo benchmark. By associating metadata with each value, CaMeL allows fine-grained security policies, effectively restricting malicious inputs without altering the LLM itself. This approach addresses the growing concern over adversarial attacks that exploit LLM vulnerabilities, demonstrating a shift toward more robust security measures in AI systems.
"CaMeL can neutralize 67% of attacks in the AgentDojo security benchmark, applying traditional software security principles to prevent prompt injection attacks in LLMs."
"CaMeL associates some metadata to every value, allowing fine-grained security policies to express what can and cannot be done with that value."
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