Secret Scanner Agent: Extracting Secrets and Access Context from Unstructured Documents

2026-07-13 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Advanced, short

Summary

Secret Scanner Agent (SSA) is a multi-agent large-language-model system designed to extract both leaked credentials (secrets) and their associated access context ("doors") from unstructured documents like emails, chat threads, and incident notes. Traditional secret scanners struggle with fragmented or reformatted credentials and do not identify the resource a secret unlocks. SSA addresses this by pairing a detection agent, which prioritizes recall, with a review agent that filters false positives and recovers missing context. Evaluated on synthetic benchmarks covering 23 secret types and multiple document formats, multi-agent SSA improves extraction precision over a single-agent variant by up to 16 percentage points, particularly for door extraction. It matches regular-expression scanners' precision while more than tripling their recall, and outperforms thirteen security analysts by being more precise, recovering nearly twice as many secret-door pairs, and running five to seventeen times faster. SSA provides actionable findings for triage and remediation by returning the secret, its door, and supporting evidence.

Key takeaway

For security engineers managing incident response, traditional secret scanners are insufficient for the complex, unstructured data common in breaches. You should integrate advanced multi-agent LLM systems like Secret Scanner Agent (SSA) to not only detect credentials but also identify the specific systems or resources they unlock. This approach significantly improves the precision and recall of secret-door pair identification, enabling faster, more effective triage and remediation of exposed access.

Key insights

Multi-agent LLM systems can accurately extract fragmented secrets and their access context from unstructured text.

Principles

• Incident response requires both secrets and their "doors."
• Unstructured documents are a significant secret leak vector.
• Multi-agent LLMs enhance precision and recall over single agents.

Method

SSA uses a detection agent for recall and a review agent for false positive filtering and context recovery.

In practice

• Identify "door" context for leaked credentials.
• Use multi-agent LLMs for complex information extraction.
• Automate secret-door pair identification in incident response.

Topics

• Secret Detection
• Credential Exposure
• Large Language Models
• Multi-Agent Systems
• Incident Response
• Information Extraction

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer, Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.