Red Teaming for AI

What is red teaming for AI?

Red teaming for AI is the practice of stress-testing artificial intelligence systems by simulating real-world adversarial tactics. It goes beyond traditional QA by intentionally probing AI models, applications, and pipelines to uncover hidden vulnerabilities before attackers can exploit them.

Unlike standard penetration testing, AI red teaming blends technical, behavioral, and adversarial methods. Teams may attempt prompt injections, model evasion, or data poisoning to test how resilient a system is under realistic attack conditions. For example:

Prompt injection testing: An AI chatbot is manipulated into bypassing guardrails by embedding malicious instructions within user queries.
Model evasion attempts: Adversaries craft inputs designed to trick image recognition systems into misclassifying objects, such as reading a stop sign as a speed limit sign.
Data integrity attacks: Poisoned training data is introduced to subtly bias recommendations or weaken fraud detection models.

These exercises reveal how AI can fail under pressure, highlighting both technical flaws and governance gaps. As AI becomes core to decision-making, red teaming ensures systems are tested against creative adversarial behavior, not just known vulnerabilities.

Objectives and scope of AI red team testing

The primary goal of red team testing in AI is to evaluate resilience under real-world conditions. Instead of simply checking whether systems meet functional requirements, red teams aim to expose how models behave when adversaries push them beyond expected use cases.

Typical objectives include:

Identifying exploitable vulnerabilities: Red teams search for weaknesses such as data leakage, model evasion, or malicious code execution paths. These exercises align with modern detection techniques like LLM code pattern malicious package detection.
Evaluating system robustness: Stress-testing responses to adversarial prompts, edge cases, and abnormal inputs reveals how well the AI holds up when manipulated.
Testing supply chain integrity: Red teams probe whether poisoned training data or malicious contributors can bypass safeguards, similar to LLM code author detection for malicious package contributions.
Assessing compliance readiness: Simulations highlight whether AI systems adhere to regulatory requirements under pressure, surfacing gaps in auditability or reporting.
Measuring organizational response: Beyond the technical layer, red teaming gauges how quickly teams detect anomalies and whether escalation procedures are effective.

The scope of AI red teaming is intentionally broad, spanning model training, deployment, monitoring, and incident response. By challenging every layer, organizations gain confidence that risks can be caught and mitigated before they reach production.

Common vulnerabilities exposed by red teaming in AI systems

Red teams probe AI across training, deployment, and runtime. Below are a few common vulnerabilities, each paired with real-world examples and a practical explanation of how a red team would test for them.

Prompt injection and jailbreaks

One of the most visible vulnerabilities in modern AI systems is prompt injection, where adversaries craft inputs to override safety filters or embedded instructions. Public jailbreaks like DAN and similar techniques have repeatedly shown how easy it is to manipulate chatbots into producing restricted outputs.

During an AI red teaming exercise, security teams simulate these attacks by embedding hidden commands or obfuscated payloads into prompts, monitoring whether the model follows instructions it should ignore.

These tests highlight weak input sanitization and reveal where system guardrails break down under pressure.

Adversarial examples and model evasion

Red teams also focus on adversarial examples: small, carefully crafted changes to inputs that cause misclassification or incorrect predictions. A well-known case involved researchers subtly altering stop signs so that image recognition systems misread them as speed-limit signs, a dangerous flaw in autonomous driving.

By generating adversarial perturbations or physical modifications, red teamers evaluate whether AI systems fail when faced with manipulated data.

These exercises expose weaknesses in preprocessing pipelines and model robustness that normal testing would miss.

Data poisoning and Trojaning attacks

Another class of vulnerabilities involves poisoning training data or embedding “Trojan” triggers into model weights. Academic studies such as BadNets demonstrate how poisoned datasets can implant hidden backdoors, causing the model to misbehave when a specific trigger appears.

In practice, this risk grows when enterprises use unvetted third-party datasets or pre-trained models. Red teaming for AI often includes injecting poisoned examples into a controlled environment to test whether the system detects anomalies.

The findings reveal how well data vetting, provenance checks, and retraining safeguards actually work in practice.

Memorization and data leakage

AI systems are also prone to memorizing sensitive data and leaking it during inference. Membership inference and model inversion attacks have shown that attackers can reconstruct whether specific records were part of training sets, or even recover fragments of the original data.

There have been reports of code-generation models outputting verbatim snippets from public repositories, underscoring the risk of unintentional leakage. A red team probes for these weaknesses by running extraction attacks against deployed models, confirming whether personal information, secrets, or intellectual property can be pulled from responses.

Supply chain and dependency risks

Finally, many vulnerabilities uncovered through red team testing originate in the software supply chain. Malicious packages on npm or PyPI, or compromised model checkpoints, can introduce hidden backdoors or vulnerabilities into AI workflows.

The event-stream compromise on npm remains a well-cited example of how subtle supply chain manipulations spread widely. Red teams replicate these scenarios by attempting to insert malicious dependencies into a staging pipeline or by validating whether artifact provenance and SBOM coverage detect the tampering.

These tests expose how resilient an organization is against upstream risks that could cascade into production systems.

Best practices and governance for red teaming AI

Effective AI red teaming is not a one-off exercise. It requires structured processes, governance frameworks, and automation to ensure vulnerabilities are consistently uncovered and addressed.

Establish a defined scope and methodology

Organizations should start by outlining what systems will be tested, acceptable attack methods, and success criteria. Clear scoping avoids wasted effort and ensures testing aligns with business risk priorities.

Blend manual and automated red teaming

Human creativity is essential for uncovering novel weaknesses, but automated techniques can continuously probe for issues at scale. Integrating automation with governance workflows helps red teams detect risky changes faster.

Integrate with the SDLC

Red teaming findings should feed directly into development pipelines. This allows vulnerabilities to be triaged, prioritized, and remediated before release, rather than relying on late-stage fixes in production.

Treat governance as a first-class concern

Testing must be paired with oversight. Organizations should align red team practices with compliance frameworks like NIST AI RMF or ISO/IEC 42001, while also ensuring results inform risk registers and board-level reporting.

Build response playbooks

It’s not enough to uncover vulnerabilities. Red teams should partner with incident response and AppSec teams to create playbooks that define how to escalate, contain, and remediate issues discovered during exercises.

By embedding these practices into governance processes, red teaming evolves from an experimental activity into a sustainable program. The result is continuous assurance that AI systems are tested against realistic threats while keeping compliance and business impact in view.

Frequently asked questions

What is the difference between AI red teaming and traditional penetration testing?

Traditional penetration testing targets known vulnerabilities in applications or infrastructure. AI red teaming simulates adversarial attacks unique to models, prompts, and data pipelines, exposing risks traditional pen testing does not cover.

How often should red teaming be conducted on AI models in production?

Frequency depends on risk profile, but most organizations benefit from at least annual AI red teaming, with additional exercises triggered after major model updates, retraining, or significant changes to architecture.

What metrics determine the success of AI red team engagements?

Success metrics include the number of exploitable vulnerabilities uncovered, response time to findings, and improvements in model robustness, governance, and detection capabilities after remediating red team–identified risks.

How can red teaming for AI help uncover bias or adversarial input vulnerabilities?

By simulating adversarial prompts and poisoned data, red teams reveal bias amplification and unsafe decision-making. These exercises help ensure fairness, reliability, and security are maintained throughout model training and deployment.

What role does governance play in ensuring safe and ethical red teaming of AI?

Governance ensures red team activities are ethical, compliant, and aligned with business goals. Strong governance integrates findings into risk registers, incident response plans, and compliance frameworks to drive sustainable AI assurance.

← Back to glossary