Follow-up: measuring LLM-agent failures with replay evidence
Source Reddit Cybersecurity
Discussion
This report discusses RedThread, an open-source command-line tool designed to support authorized red-team campaigns against large language model (LLM) agents. The tool helps measure and produce replayable evidence of failures in LLM-agent behavior, focusing on repeatability and actionable findings rather than preventing prompt injection attacks. It provides adversarial campaign traces, metadata, scoring rubrics, and replay capabilities to assist security reviewers and developers in evaluating AI-agent vulnerabilities. The tool is intended for staging and evaluation, not for direct production defense.
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Follow-up on RedThread, an open-source CLI for authorized LLM/agent red-team campaigns.
Repo: https://github.com/matheusht/redthread
I have a demo campaign result now: 3 runs, 33.3% ASR, one SUCCESS, one PARTIAL, one FAILURE.
The security angle is not “prompt injection exists.” It is how to produce evidence that a prompt/tool/action failure is repeatable and worth fixing.
RedThread focuses on: - adversarial campaign traces - tactic/persona metadata - judge/rubric scoring - exploit replay - benign replay - candidate defense synthesis
No claim that it prevents prompt injection in production. It is a staging/evaluation tool for builders and security people.
For security reviewers: what would you want in a report before accepting an AI-agent finding as actionable?
Links cited in the discussion
Also discussed in r/AskNetsec, r/Information_Security