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Researchers introduce a 21-scenario benchmark evaluating LLM-based agents against adaptive multi-round attacks that pivot based on prior defender responses. This approach highlights vulnerabilities in memoryless LLM defenders exposed to prompt injection and multi-turn manipulation.
Researchers introduce a 21-scenario benchmark evaluating LLM-based agents against adaptive multi-round attacks that pivot based on prior defender responses. This approach highlights vulnerabilities in memoryless LLM defenders exposed to prompt injection and multi-turn manipulation.
AI-assisted summary based on the listed source.
LLM-based agents process external content, exposing them to prompt injection and multi-turn manipulation. Most safety benchmarks evaluate defenders against fixed attack pools collected before evaluation, single-turn or multi-turn. We present a 21-scenario benchmark for \emph{adaptive multi-round attacks against...
Current safety benchmarks often rely on fixed attack pools and single-turn evaluations, which may not capture real-world adaptive threats. This new benchmark provides a more rigorous test for improving the robustness of LLM agents in dynamic adversarial settings.
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