Red Teaming and Adversarial Testing

Red teaming is the practice of deliberately probing AI systems for vulnerabilities — trying to make them produce harmful, biased, or policy-violating outputs. Unlike standard evaluation that assesses typical usage, red teaming simulates adversarial users, edge cases, and creative attacks. It is one of the most important safety mechanisms in modern AI development.

What Red Teamers Do

Red teamers think like attackers:

Craft prompts designed to bypass safety filters
Use roleplay, hypotheticals, and fiction to elicit prohibited content
Chain multiple prompts to gradually steer the model off course
Test multilingual attacks (safety filters may be weaker in non-English languages)
Encode harmful requests in indirect formats: base64, acronyms, reversed text, code comments
Exploit model helpfulness — "I need this for my novel / research / homework"

Types of Red Team Campaigns

Broad exploration. Diverse attacks across many risk categories to map the vulnerability landscape.

Targeted campaigns. Focused testing on specific concerns: election misinformation, biosecurity, child safety, or a newly discovered jailbreak technique.

Regression testing. After safety patches, re-run known attacks to verify fixes hold.

Automated + human hybrid. Scripts generate candidate attacks; humans evaluate results and craft follow-ups.

Documenting Red Team Findings

Effective red team reports include:

Attack prompt (exact text used)
Model response (exact output, including partial compliance)
Risk category (which policy area is implicated)
Severity (how harmful would this be if a real user received it?)
Reproducibility (does the attack work consistently or intermittently?)
Suggested mitigation (if you have ideas for blocking this attack pattern)

Ethical Boundaries for Red Teamers

Red teaming requires generating harmful content as part of the test. Platform policies and task guidelines define what you may generate, how to handle findings, and when to stop. Never share red team results outside authorized channels. The goal is finding vulnerabilities so they can be fixed, not demonstrating harmful capabilities publicly.

Skills That Transfer

Red teaming develops critical evaluation skills: creative thinking, policy knowledge, attention to subtle compliance failures, and systematic documentation. These skills are highly valued in AI safety roles and represent a career growth path for experienced AIDASH workers.

Key Takeaways

Red teaming proactively attacks AI systems to find safety vulnerabilities before users do
Attacks use roleplay, encoding, multilingual prompts, and gradual escalation
Document findings with exact prompts, responses, severity, and reproducibility
Follow platform ethical guidelines — findings are for fixing vulnerabilities, not public demonstration