What is AI red teaming?

AI red teaming is the practice of deliberately attacking an AI agent, chatbot, or voice agent to find where it breaks before a real user or bad actor does. Autonomous red team agents run prompt injection, jailbreak, data exfiltration, and other adversarial scenarios against your agent, then score every response and hand you a letter grade. The OWASP Top 10 for LLM Applications (2025) ranks Prompt Injection as LLM01, the top risk category for LLM-backed applications, which is why it is the first category TestMu AI red teaming runs test for.

What attack categories does AI red teaming cover?

Nine categories: prompt injection, jailbreak, data exfiltration, PII leakage, harmful content, overreliance, hijacking, policy violation, and technical injection. Each category runs as a distinct set of adversarial scenarios against your agent, scored independently and rolled up into an overall grade.

What intensity levels can I run?

Three: basic, intermediate, and advanced. Basic covers common, low-effort attack patterns. Advanced pushes multi-turn, chained, and obfuscated attacks closer to what a determined adversary would attempt. Run basic in early development and advanced before a production release.

How does the AI red teaming CLI work?

The testmu-a2a-cli package ships a dedicated redteam command: testmu-a2a redteam --agent https://my-bot.com/api/chat --intensity advanced --spec "Banking customer support agent". Target specific categories with --categories prompt-injection,jailbreak,pii-leakage. The same CLI also runs standard agent testing evaluations, so red teaming and functional testing share one toolchain. See the testmu-a2a-cli documentation for the full command reference.

What does an AI red team report contain?

Every run produces a letter grade (A+ through F) with a per-category breakdown, showing which of the 9 attack categories passed, partially passed, or failed, how many scenarios ran per category, and the confidence level behind each score. Failing scenarios include the full transcript and the evidence that drove the verdict.

Can AI red teaming run in CI/CD?

Yes. Export results as table, JSON, or JUnit XML and gate a build on the CLI's exit code (0 for pass, 1 for failures or an error). JUnit output works natively in GitHub Actions, GitLab CI, Jenkins, and CircleCI. Runs execute on HyperExecute, with secure tunnels available for firewall-restricted agent endpoints.

Does AI red teaming also check for bias and hallucination?

Yes. The same 9 quality metrics used in standard chatbot testing and voice agent testing, including bias detection and hallucination detection, are stress-tested under adversarial pressure during a red team run, not just under clean, well-formed inputs.

Is AI red teaming compliant with security standards?

TestMu AI is SOC 2 Type II certified, ISO 27001 certified, GDPR and CCPA compliant, NIST 800-53 Rev 5 compliant, and HIPAA certified. Every red team run produces an exportable, auditable report with scenario transcripts, category scores, and timestamps for compliance documentation.

Who needs AI red teaming?

Teams shipping any customer-facing AI agent, chatbot, or voice agent that handles sensitive data, financial decisions, or regulated conversations. It pairs naturally with agent testing for functional coverage, so security and quality gates run from the same platform before every release.

How do I get started with AI red teaming on TestMu AI?

Create a free account, connect your agent endpoint, and run testmu-a2a redteam against it at basic intensity to get a baseline grade. Add custom categories and advanced intensity as you harden the agent, then schedule recurring runs to catch regressions. Explore agent testing and KaneAI to author and evolve your tests in natural language.

AI Red Teaming for Every Agent You Ship

Deploy autonomous red team agents that attack your chat, voice, and phone agents with prompt injection, jailbreaks, and data exfiltration attempts, then score every response and hand you a letter grade before attackers find the gaps.

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Red Team Every Agent Before It Reaches Production

AI-native evaluators that simulate real attacks, score every response, and grade your agent's resistance across 9 categories.

Automated Attack Simulation

Specialized Security Evaluators

Scaled Red Teaming on HyperExecute

Automated Attack Simulation

Run prompt injection, jailbreak, and data exfiltration attempts against your chat, voice, or phone agent from the CLI or dashboard, at basic, intermediate, or advanced intensity.

9 Attack Categories

Probe prompt injection, jailbreak, data exfiltration, PII leakage, harmful content, overreliance, hijacking, policy violation, and technical injection.

3 Intensity Levels

Run basic, intermediate, or advanced attack intensity to match how hard a real adversary would push.

CLI or Dashboard

Trigger a full red team run with testmu-a2a redteam --agent <url> --intensity advanced, or launch the same run from the UI.

From First Attack Attempt to Production Gate

Confidence Scales With Evaluation Volume

Every category score ships with a High, Medium, Low, or Very Low confidence level based on how many scenarios ran, so a passing grade with High confidence means something different from Low.

9 Metrics Score Every Adversarial Turn

The same bias, hallucination, and completeness metrics used in standard evaluations get stress-tested under prompt injection, jailbreak, and hijacking attempts.

See Exactly Where a Run Lands

Each red team run rolls up into a 0-100 score across four weighted dimensions, so you know how close an agent is before you dig into transcripts.

Every Attack Turn: Matches and Discrepancies

Pinpoint exactly which attack succeeded and which failed, tracked as Pass, Fail, or Partial against your defined validation criteria.

Built for Every Layer of AI Red Teaming

Project & Environment Management

Create agents, manage test environments, and scope variables with bulk creation support.

Test Profiles & Personas

Inject reusable key-value test data and run adversarial scenarios across a pre-built or custom persona library.

Validation Criteria

Define custom, evidence-based pass/fail rules per attack scenario with High/Medium/Low confidence tracking.

Security & Infrastructure

Execute via HyperExecute with optional secure tunnels for firewall-restricted agent endpoints.

Scheduling Engine

Automate red team runs using preset frequencies or full custom cron expressions with IANA timezone support.

Go-Live Assessment

Get a Green, Yellow, or Red verdict from four weighted dimensions with AI-powered failure-pattern analysis and prioritized action items.

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