Red-Team Your Agent with Adversarial Prompts

PROJECT CONTEXT:
I have never seriously attacked my own agent, so I do not know how it fails under pressure. LLM red-teaming frameworks provide dozens of vulnerability probes and attack methods to test agents adversarially before attackers do.

GOAL:
Stand up an adversarial testing program: a threat-driven probe set, automated attack execution, scored results, and a remediation loop.

REQUIREMENTS:
- A prioritized vulnerability list mapped to my agent's actual risks
- Attack methods per vulnerability (direct, obfuscated, multi-turn, role-play)
- Automated execution against the agent with pass/fail scoring
- Severity-ranked findings with reproductions
- A remediation loop and re-test cadence, wired into releases

CONSTRAINTS:
- Red-teaming targets MY agent's real capabilities and data, not generic model chat
- Successful attacks are logged as security findings with reproductions, then fixed

DEFINITION OF DONE:
- The probe set covers my top vulnerability classes with multiple attack methods each
- Attacks run automatically and produce a scored, reproducible report
- Every high-severity finding has a fix and a regression test
- The suite re-runs on release and blocks on high-severity regressions

COMMON FAILURES TO AVOID:
- Testing only the happy path and calling the agent secure
- One-off manual pokes with no reproductions or scoring
- Finding vulnerabilities and never closing them
- A red-team run once, never repeated as the agent evolves