Auditing agentic LLM APIs solely via text-output channel
Do not rely only on text output for auditing agentic LLM APIs, as tool calls discard text and system prompts can distort output.
Use action-based audits that probe the model's tool-call distribution, which is exposed by the serving stack and invariant to deployment context.
Auditing agentic LLM APIs solely by examining the text-output channel.
For agentic APIs, modern serving stacks discard text output when a tool is called. Provider-injected system prompts can also distort text distributions, leading to false accusations of model substitution.
Avoid auditing agentic LLM APIs only through their text output. This channel is unreliable because tool calls discard text, and system prompts can manipulate output, leading to inaccurate audit results.
- AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes ↗arXiv · arXiv cs.AI · Aug 30, 2026 · paraphrased
First seen on Subvolts Aug 30, 2026. Updated Sep 11, 2026. Spotted a problem? Tell us.
- AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy ProbesAnthropic · arXiv cs.AI · Aug 30, 2026
- AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy ProbesGoogle · arXiv cs.AI · Aug 30, 2026
- AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy ProbesOpenAI · arXiv cs.AI · Aug 30, 2026
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- Which AI tools does this work with?
- Any current model. It was seen with in the sources.
- Where did this come from?
- arXiv (arXiv cs.AI). Subvolts collected, normalized and credited it; the link above opens the original.