Red Teaming

Benched.ai Editorial Team

Red teaming is the practice of systematically probing an AI system with adversarial prompts to uncover safety, security, and ethical weaknesses before deployment.

Red Team Workflow

Phase	Goal	Example Activity
Scoping	Define threat model	Select disallowed content categories
Attack design	Craft adversarial prompts	Jailbreak, prompt injection
Execution	Run attacks at scale	Automated fuzzing harness
Triage	Classify failures	Toxicity, privacy leak
Mitigation	Patch model or filters	Fine-tune, adjust moderation threshold

Common Attack Vectors

Prompt injection via system role override.
Encoding tricks (zero-width, homoglyph) to bypass filters.
Long-context dilution to smuggle disallowed content.

Design Trade-offs

Extensive red teaming increases upfront cost but reduces post-launch incidents.
Fully automated attacks miss nuanced harms; human-in-the-loop review needed.

Current Trends (2025)

Community red team bounty programs similar to bug bounties.
Shared adversarial corpora (JailbreakBench) standardize evaluation¹.
Differential privacy scoring detects PII leak probability during red team runs.

Implementation Tips

Freeze evaluation data; changing attacks mid-run hides regressions.
Track attack success rate and severity over time as key risk metric.
Retest after every model or policy update.

Anthropic Research, Benchmarking Large Language Model Jailbreaks, 2025. ↩