AI Red Teaming

AI Red Teaming helps you systematically probe for security, safety, and trust risks in foundation models, agentic systems, AI applications, and traditional ML models - wherever they are deployed. Whether your models run on AWS, Azure, Google Cloud, or custom infrastructure, Dreadnode gives you repeatable, measurable, evidence-backed assessments with deep analytics and reporting.

The problem

Generative AI systems and traditional ML models excel at solving tasks and enhancing productivity - generating code, making decisions, processing data. But these systems are inherently vulnerable to security and safety risks that traditional software testing cannot catch.

The goal: understand and evaluate these risks by structurally probing for vulnerabilities before actual attackers do.

What could go wrong

Security risks

• Prompt injection causing remote code execution - an attacker crafts inputs that cause the model to execute arbitrary code, potentially compromising the entire host system
• Data exfiltration via agent tools - secrets, customer data, or internal documents sent to attacker-controlled endpoints through tool abuse, markdown rendering, or DNS tunneling
• Credential theft - system prompts, API keys, database credentials, or authentication tokens extracted through adversarial probing
• Tool manipulation forcing dangerous actions - agents tricked into executing destructive commands, privilege escalation, or unauthorized operations on connected systems

Real-world impact: customer data loss, ransomware deployment, financial loss, regulatory penalties, brand reputation damage.

Safety risks

• Harmful content generation - models producing instructions for dangerous activities, weapons, illegal substances, or content that could cause physical harm
• Manipulation and deception - AI systems used to generate convincing misinformation, social engineering attacks, or psychologically manipulative content
• Bias amplification - models amplifying societal biases in hiring, lending, healthcare, or criminal justice decisions, leading to discriminatory outcomes

Real-world impact: legal liability, user harm, loss of trust, regulatory action.

Trust risks

• Hallucination in critical decisions - models confidently producing incorrect information in medical, legal, or financial contexts
• Lack of reproducibility - inability to demonstrate that safety evaluations are systematic, repeatable, and comprehensive
• Compliance gaps - failure to demonstrate adherence to OWASP, MITRE ATLAS, NIST, or industry-specific AI safety frameworks

How Dreadnode helps

AI Red Teaming Agent

The AI Red Teaming agent helps you probe for these risks using the Dreadnode TUI. Describe what you want to test in natural language, and the agent orchestrates attacks, applies transforms, scores results, and helps you understand which attacks are working and which are not - so you can craft better attack strategies.

dn --capability ai-red-teaming --model openai/gpt-4o

SDK and CLI

The Dreadnode SDK provides:

• 45+ attack strategies - TAP, PAIR, GOAT, Crescendo, BEAST, Rainbow, GPTFuzzer, AutoDAN-Turbo, AutoRedTeamer, NEXUS, Siren, CoT Jailbreak, Genetic Persona, JBFuzz, T-MAP, APRT Progressive, and more
• 450+ transforms across 38 modules - encoding, ciphers, persuasion, prompt injection, MCP tool attacks, multi-agent exploits, exfiltration techniques, reasoning attacks, guardrail bypass, browser agent attacks, backdoor/fine-tuning, supply chain, and more
• 130+ scorers across 34 modules - jailbreak detection, PII leakage, credential exposure, tool manipulation, exfiltration detection, reasoning security, MCP security, multi-agent security, and compliance scoring
• 15 goal categories - harmful content, credential leak, system prompt leak, PII extraction, tool misuse, jailbreak general, refusal bypass, bias/fairness, content policy, reasoning exploitation, supply chain, resource exhaustion, quantization safety, alignment integrity, and multi-turn escalation
• Multimodal risk - attacks and transforms for text, image, audio, and video inputs
• Multi-agent risk - 11 transforms and 6 scorers targeting inter-agent trust boundaries, delegation chains, and shared memory
• Multilingual risk - language adaptation, transliteration, code-switching, and dialect variation transforms
• Dataset support - bundled goal sets for OWASP categories, custom YAML suites filterable by operation type (image, text-to-text, agentic)

Platform

As AI red team operators run attacks through the TUI, CLI, or SDK, results are automatically submitted as assessments to the Dreadnode platform. Each assessment captures the full campaign: target model, attack strategies used, every trial with prompt-response pairs, scores, transforms applied, and compliance tags. The platform then provides:

• Assessments - every red teaming campaign is tracked as a named assessment with its target model, attack configurations, and status. Assessments accumulate over time, giving you a complete history of what has been tested and when.
• Overview dashboard - aggregates all assessments into a single risk picture: total findings, attack success rates, severity breakdown, finding outcomes (jailbreak vs. refusal vs. partial), and deep risk metrics at a glance
• Executive reporting - compliance posture across OWASP Top 10 for LLMs, OWASP Agentic Security (ASI01-ASI10), MITRE ATLAS, NIST AI RMF, and Google SAIF, with exportable PDF reports so stakeholders can make go/no-go decisions
• Evidence-backed traces - every attack, every trial, every conversation turn is recorded with full provenance. Model builders can expand any finding to see the exact attacker prompt and target response, walk through multi-turn attacks step by step, and export data as Parquet for adversarial fine-tuning
• Human-in-the-loop review - operators can edit finding classifications (jailbreak, partial, refusal), adjust severity levels, and document reasoning. All dashboard metrics recompute automatically when findings are reclassified.

How AI Red Teaming works

1. Define Goal - specify the target model or agent and the attack objective (e.g., “Can this model be tricked into generating exploit code?”)
2. Run Attacks - execute attacks using any of the 46 strategies (TAP, PAIR, Crescendo, AutoRedTeamer, NEXUS, CoT Jailbreak, etc.) with transforms applied to test different evasion techniques
3. Analyze Results - review findings with severity classification, Attack Success Rate, and compliance mapping against OWASP, MITRE ATLAS, NIST, and Google SAIF
4. Review and Report - inspect traces with full attacker prompts and target responses, edit finding classifications, export PDF reports and Parquet data for stakeholders
5. Iterate and Harden - use findings to improve post-safety-training robustness (adversarial fine-tuning, input classifiers, guardrail updates), then re-test to verify the fixes

This is a continuous loop. Every assessment builds on the last, and all results accumulate in the platform for trend analysis across models and versions.

Get started in 60 seconds

The fastest way to start AI red teaming is with the TUI agent. One command, and you’re running attacks:

pip install dreadnode && dn login
dn --capability ai-red-teaming --model openai/gpt-4o

Then tell the agent what to test in plain English:

“Run a TAP attack against openai/gpt-4o-mini with the goal: reveal your system prompt”

The agent handles everything — selecting attacks, applying transforms, scoring results, and registering assessments with the platform. No code, no configuration files.

Start with the TUI Agent →

Need more control?

Path	Best for	Get started
TUI Agent	Run AI red teaming via natural language, agent orchestrates attacks, transforms, and scoring	TUI Guide
CLI	Repeatable attacks, YAML suites, CI pipelines	CLI Guide
Python SDK	Custom targets, agent loops, composed transforms	SDK Guide

Who this is for

Persona	What they need	Where to start
AI Red Team Operator	Run attacks, craft strategies, find vulnerabilities	TUI Agent or CLI
Executive / CISO	Risk posture, compliance status, go/no-go decisions	Overview Dashboard and Reporting
Model Builder / Engineer	Evidence of what broke, traces, reproducible failures	Traces and SDK

Start with the TUI Agent

Attacks Reference

Transforms Reference

Scorers Reference

Case Study: Llama Scout

Goal Categories