[2607.02121] Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring

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arXiv:2607.02121 (cs)

[Submitted on 2 Jul 2026]

Title:Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring

Authors:William Hackett, Peter Garraghan<br>View a PDF of the paper titled Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring, by William Hackett and Peter Garraghan

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Abstract:As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Guardrail systems that detect and block malicious instructions sent to and from an LLM are an essential component of AI security. However, researchers conducting black-box adversarial emulation against production AI systems often struggle to determine whether a guardrail block or an LLM rejection has occurred. This distinction is important because the techniques used to bypass guardrails can differ substantially from those used to bypass LLM safety alignment, and has a material impact on attack technique selection and optimization. We propose the first black-box guardrail reconnaissance methodology, which detects the presence of a guardrail within a target AI system through behavioral monitoring of HTTP, lexical, and timing signals, assuming only black-box access and zero prior knowledge of the guardrail or AI system. Experiments demonstrate that our approach detects guardrail presence with 100% accuracy, with statistically significant behavioral separation between benign and malicious interactions (q

Comments:<br>19 pages, 13 figures, 4 tables

Subjects:

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)

Cite as:<br>arXiv:2607.02121 [cs.CR]

(or<br>arXiv:2607.02121v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2607.02121

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: William Hackett [view email]<br>[v1]<br>Thu, 2 Jul 2026 12:59:28 UTC (1,091 KB)

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