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Attack Success Rate in AI Security Is a Family of Metrics, Not a Single Number

The commonly used Attack Success Rate (ASR) metric in evaluating attacks and defenses on LLM agents is not a single fixed value but a family of metrics influenced by six design choices. These choices are rarely specified or held constant across studies, challenging the validity of ASR comparisons.

Source: arXiv · arxiv.org Published 2026-09-21T14:16:59+00:00 Detected 2026-09-23T05:24:32+00:00
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The commonly used Attack Success Rate (ASR) metric in evaluating attacks and defenses on LLM agents is not a single fixed value but a family of metrics influenced by six design choices. These choices are rarely specified or held constant across studies, challenging the validity of ASR comparisons.

AI-assisted summary based on the listed source.

Attack success rate (ASR) is the headline metric in nearly every published evaluation of attacks on, and defenses for, LLM agents. We argue that ASR as currently used is not a single quantity but a family of metrics parameterized by six design choices that papers seldom specify and never hold constant across the...

Understanding that ASR varies based on underlying design parameters highlights the need for clearer reporting and standardization in AI security evaluations. This insight can improve the reliability and comparability of research on attacks and defenses for agentic AI.

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Signal Strength 95% Technical label SOURCE-BACKED Public Interest 29 Category MONEY Reader Depth GENERAL

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Public Interest components
Recognizable Entity Score 0 Practical Impact Score 8 Novelty Interest Score 72 Consequence Score 46 Curiosity Score 16 Shareability Score 42

VQV surfaced this signal because it is recent, relevant to AI Security, connected to arXiv.