AI Red Teaming: How to Continuously Test and Secure Agentic AI Systems

As more organizations do AI more widely, a fundamental problem emerges: how do you secure systems that don't repeat themselves?

Security testing methods have been designed for predictable software, where the same input always yields the same output. AI systems don't behave this way. They output probabilistic responses and respond according to context.

Attackers exploit this by making small variations to inputs.

The danger is now concrete. According to Adversa AI, a 2025 security report revealed that a third of real-world AI security flaws were triggered by basic prompt manipulation, and some of them have already led to damages of over USD 100,000. For instance, on the day GPT-5 launched in January 2026, red teams exposed jailbreak techniques within 24 hours.

AI systems also bring fresh attack vectors - prompting injection, training data poisoning, model extraction, and tool abuse - demanding testing approaches that are different from standard penetration testing. This AI red teaming guide offers security practitioners a framework for understanding AI red teaming and adopting it throughout the AI lifecycle, covering scoping, threat modeling, execution, validation, and ongoing oversight.

In this blog, explore the essentials of AI red teaming for agentic AI systems. Learn how to identify vulnerabilities, prevent prompt injection and data poisoning, and apply best practices to keep AI models secure and reliable.

What is Red Teaming in AI?

AI red teaming is the practice of adversarial testing tailored specifically for AI systems to uncover vulnerabilities, safety weaknesses, and security gaps before they are exploited in real-world environments.

Unlike traditional red teaming, which focuses on networks, infrastructure, and application layers, AI red teaming targets the distinct attack surfaces introduced by machine learning systems. These include training data integrity, inference pipelines, prompt handling, model outputs, system integrations, and the model's behavior.

By focusing on these aspects of AI, red teams can reveal potential security issues that standard security evaluations miss, thereby making the AI system more secure and reliable.

AI Safety vs AI Security in Red Teaming

The distinction between AI safety and AI security is one of the most important foundations in AI red teaming.

AI safety testing focuses on protecting people and society from harm caused by AI systems. It examines how a model behaves and whether its outputs create risk. Safety testing typically includes evaluating:

• Bias and discriminatory outputs

• Hallucinations and factual inaccuracies

• Harmful or unsafe content generation

• Scenarios where the system could be misused

The goal is to ensure AI systems behave responsibly and align with ethical, legal, and societal expectations.

AI security testing, by contrast, focuses on protecting AI systems from external threats. It evaluates how attackers might manipulate, extract, or compromise models. Security testing includes assessing exposure to:

• Prompt injection attacks

• Data exfiltration attempts

• Model manipulation and evasion

• Unauthorized access to training data or model weights

Here, the objective is to defend the AI system itself from adversarial actors.

Leading AI labs demonstrate how both dimensions must work together. Anthropic’s red teaming methodology, for example, integrates domain specialists, including trust and safety experts, national security professionals, and multilingual testers, to probe both behavioral risks and technical attack surfaces.

Good AI red teaming programs are naturally both safety and security-focused, for the simple reason that an adversary will take the attack path that is easiest. A safety gap that allows potentially harmful output is a weaponizable attack surface. A security breach that reveals training data poses a significant privacy, confidentiality, and trust concern.

As AI systems become integral to the keystones of our workflows, conventional behavioral threat detection approaches will need to extend to handle attack modalities unique to AI. Red teaming, too, will need to develop apace - learning to treat safety and security not as two distinct exercises, but as nested layers of resilience.

AI Red Teaming Process

An effective AI red teaming process follows a structured methodology that adapts traditional security testing practices to the distinct behavior of AI systems.

1. Scope and Plan

Scope the AI system. Determine threat models, use cases, systemic dependencies, data sources, integrations, and testing goals. Given the emergent behavior of AI systems, the scope should include both in-scope and out-of-scope behaviors.

2. Develop an Adversarial Strategy

Map categories of probable attack vectors according to system modality. An LLM, an autonomous agent, and a multimodal AI system have distinct profiles of threats. Attack strategies will be built according to the most probable, exploitable, and disruptive attack path.

3. Execute Testing

The testing approach depends on the purpose. Manually adversarial probing, test by automated attack simulation, or a combination of human-in-the-loop may be used. The most oftengaged types of penetration testing are:

• Discovery testing to identify critical vulnerabilities

• Exploitation testing to assess weaponization

• Escalation testing to measure privilege expansion

• Persistence testing to examine long-term access

4. Document Findings

Develop robust, repeatable tests, illustrations of evidence, and an impact analysis. Since Artificial Intelligence-generated data can differ, record the specific prompts, system versions used, system state, and environment conditions that led to successful weaponisation of the exploits.

5. Validate Mitigations

After verifying the controls, run the same tests again to confirm that the exploit's advantage is mitigated and that the system has not been compromised by new conditions that introduce new vulnerabilities.

6. Implement Continuous Monitoring

The above methods lead to iterative modifications of the AI, whether through retraining, fine-tuning, or updates to integration. Develop a continual testing rhythm congruent with the model's lifecycle rather than a one-off evaluation. Microsoft's AI Red Team documentation has a comprehensive articulation of this process.

Their team, building on the earlier work of the Azure Developer Security team, developed the PyRIT (Python Risk Identification Tool for generative AI). This tool facilitates scaling a generative AI-based red team operation. It allows automated adversarial testing to be of certain outcomes, provides structured report templates, and ensures reproducibility of risk testing.

Manual vs Automated AI Red Teaming

The discussion around manual versus automated AI red teaming has matured into a practical conclusion: effective programs use both.

Manual AI Red Teaming

The unique identification of new, complex vulnerabilities is still the forte of manual testing. Human testers use their human features: creativity, reasoning in nuances, and a good dose of intuition to test variants of language, cases of manipulation, or indirect attack paths, none of which are fixed in the lists of attacks predefined in Automated Power.

Results from arXiv indicate human-designed attack methods beat numerous automated approaches during initial discovery. Attacks with roleplay cost 89.6% success. Logic trap strategies achieve nearly 81.4% success.

Encoding methods attain approximately 76.4%. Human intelligence and reasoning are essential for conceiving, adapting, and real-time adjustment. During initial testing, fresh new model deployment, or deploying a highly sensitive system where unknown flaws are prone to exist, manual testing can be highly beneficial.

Automated AI Red Teaming

Automated testing offers scale, repeatability, and regression monitoring. Adversarial prompts can be run at scale across many model versions, maintaining comprehensive baseline evaluations and alerting to erosion of security controls due to updates. Giskard's GOAT research showed up to 97% success on small models with 5 turns using automated multi-turn attack strategies.

Automated tools are best at exhaustively testing for known attack vectors and their emergence through retraining or remodelling.

Automated testing is essential for continuous validation and lifecycle monitoring.

Recommended Approach: Hybrid Testing

Industry guidance, including from Microsoft, has advocated a phased approach:

1. Run a manual red team first to identify the most damaging attack patterns for the system.

2. Convert those attack patterns, once proven, into test cases.

3. Automated test cases can then be scaled, monitored, and run over time as regression tests.

Hybrid human-in-the-loop models. Automated systems produce candidate exploits derived from learned patterns, and human analysts analyze the results and filter them to high-value targets. Reflects the changing role of modern security operations.

Automation performs at scale against well-understood threats, while human analysts analyze evolving/spearphished attack methods, for example.

For organizations building AI threat defense programs, hybrid red teaming is not optional. It is the standard for maintaining resilience in dynamic AI environments.

How AI Red Teaming Differs from Traditional Red Teaming?

AI Red Teaming vs Penetration Testing

Because both are adversarial exercises, the link can be confusing. Nevertheless, penetration testing and AI red teaming are quite different, even though both are adversarial exercises. The scope of penetration testing encompasses infrastructure, application layers, APIs, and network layers.

The PT tests for known classes of vulnerabilities, such as misconfigurations, injection flaws, access control flaws, and end-of-life software versions. The scope is well-defined, and the goal is to identify exploitable vulnerabilities so they can be ranked by impact and remediated.

Red Teaming for AI functions on a different layer. Instead of general infrastructure, they look at the model's behavior, the integrity of the training data, the inference pipeline, prompt handling, and attack points specific to AI. The red teamers try to make a system behave in unsafe ways (variously called 'misuse' or 'misalignment', or 'misbehavior'), despite robust infrastructure.

This could involve prompt injection, jailbreak attempts, adversarial attack vectors, corrupting training data sets, manipulating autonomous agents, even if the farm where the infrastructure sits is resilient.

Common AI Red Teaming Techniques

Here are the common AI red teaming techniques:

Prompt Injection Attacks

Prompt injection tricks a Large Language Model (LLM) into ignoring its original developer's instructions and instead executing instructions embedded in the user input.

• Direct Prompt Injection - The attacker directly types malicious commands into the prompt field (If there is one), e.g., "Ignore all previous instructions and..."

• Indirect Prompt Injection - The attacker conceals malicious instructions by hiding in the data the system feeds to the artificial intelligence, e.g., in a website, document, or email.

• Payload splitting - Breaking a malicious prompt into several parts, both valid by themselves, but a malicious one when combined within the model.

Jailbreaking Large Language Models

Jailbreaking is the art of bypassing the safety, ethical, and content policies baked into an AI model. It often involves psychological manipulation or role-playing to trick the model into abandoning its guardrails.

• Roleplay/Persona Attacks - Directing the model to portray a character or persona that has no restrictions (e.g., theDO ANYTHING NOWorDANprompt).

• Logic Traps - Providing prompts with complex or conflicting instructions that require the model to break its safety policy.

• Multilingual/Obfuscation - Masking malicious prompts in foreign languages, Base64, or other formats.

Adversarial Input Manipulation

This method consists of slight, intentional corruptions of input data (text, images, audio, etc.) that lead a model to produce false predictions/classifications.

• Evasion Attacks: Changing input data at inference time in order to fool a filter or mislead a given model

• Multimodal Attacks: Incorporating instructions into images or audio that a multimodal model (e.g., placing text within an image to instruct the AI to ignore its rules)

Model Inversion Attacks

• Model inversion: Reverse-engineering the AI model to reveal private training information and, in some extreme cases, reveal the entire model.

• PII Leakage: Using model queries to trick the model into spilling private personally identifiable information in its training data.

• Prompt leaking: Using prompts, or instructions given to the model, to reveal system prompts.

Data Poisoning Attacks

Data poisoning interferes with the training of the AI system; the attacker introduces malicious, biased, or corrupt data into the training set. It may degrade the accuracy of the model or introduce vulnerabilities or back doors that an attacker can enable later.

Bias and Toxicity Testing

Red teamers are trying to push models into producing biased, unfair, or unsafe results (e.g., racism, sexism, hate speech, dangerous instructions). Examples include:

Boundary Testing of Content: For example, finding words that the model is willing to use in combination with profanities or violent invocations.

Hallucination induced Confabulation: intentionally providing hallucinatory prompts to the model so that it enters "confabulation", babbling falsehoods confidently as facts.

AI Red Teaming Tools and Platforms

Effective AI red teaming depends on strong tools and platforms that automatically run attack simulations, closely analyze model behavior, and reveal hidden risks across prompts, responses, and connected systems.

Akto

Akto AI Agent Security provides a comprehensive platform for continuous red teaming, Agentic AI security, and MCP security in modern AI systems. It is designed to protect agentic AI architectures, where autonomous agents interact with tools, data sources, and other systems to make decisions and take actions. The platform simulates real-world AI attack scenarios, including prompt injection, agent manipulation, data leakage across workflows, and unsafe or unintended agent outputs, delivering actionable findings and remediation guidance.

By continuously monitoring AI agent interactions and MCP-based workflows throughout development and production, Akto enables teams to maintain full visibility into AI risks, enforce AI guardrails, and safely scale autonomous AI systems with confidence.

Garak

Garak enables systematic stress testing of language models with custom adversarial scenarios and attack libraries. The platform supports large-scale evaluation of prompts and model responses. It helps uncover vulnerabilities that traditional testing misses.

PromptFoo

PromptFoo specializes in prompt-level testing and evaluation for AI models. It helps red teamers define, run, and report on prompt test suites that target safety, policy boundaries, and misuse. PromptFoo supports automated regression testing for evolving models.

Enkrypt AI

Enkrypt AI provides advanced AI red teaming solutions to identify vulnerabilities in AI models and systems. It simulates real-world attack scenarios, including prompt injection, data leaks, and model manipulation. By stress-testing AI, Enkrypt AI helps organizations strengthen security, ensure safe outputs, and maintain trust in AI-driven workflows.

Mindgard

Mindgard focuses on governance-driven AI risk assessment with built-in red teaming workflows. It combines AI evaluation, policy enforcement, and compliance reporting. Security and ML teams use it to operationalize continuous AI red teaming across deployments.

AI Red Teaming Frameworks & Best Practices

To accurately red-team AI systems at scale, organizations require a repeatable process. Multiple retraining, fine-tuning, and integrations with new tools are common, but a disciplined red teaming process ensures models keep up against adversarial attacks and the exploitation of biases, misconfigurations, and non-compliance.

Step 1: Define the Scope of AI Red Teaming

Before testing begins, clearly define what is in scope.

Determine what parts of the AI system to evaluate:

• AI model stability and response

• APIs and integrations to third-party systems

• Machine learning applications in the cloud

• Data pipeline and training sets

Model realistic attack challenges:

• Evasion and data poisoning attack by adversarial machine learning

• Jailbreak attacks using injection prompts

• APIs exploitation for data exfiltration

• Supply chain weaknesses in model dependencies

Determine applicable security and compliance requirements:

• OWASP AI Security guidance

• NIST AI Risk Management Framework

• EU AI Act

• SOC 2, GDPR, and related privacy standards

Scoping must also account for unintended behaviors and emergent risks, especially in autonomous or agent-based systems.

Step 2: Select and Implement AI Adversarial Testing Methods

AI red teaming is thus distinct from typical penetration testing, which needs to employ adversarial machine learning techniques based on the task model behavior and data integrity.

Model-Centric Testing (robustness evaluation)

• Adversarial perturbation testing: Craft adversarial inputs to cause model misclassification or unsafe outputs.

• Model inversion and extraction: Try to recover training data or model parameters.

Data Pipeline Security Testing

• Data poisoning deployments: Introduce malicious training data to test for drift in behavior.

• Bias & fairness testing: Determine if an adversary can leverage bias to bias the model or amplify sensitive generated output.

Human-AI Interaction and API Security

• Prompt injection testing: Determine whether guards can be avoided using carefully engineered prompts.

• API abuse testing: Test for issues such as unrestricted data consumption or lapses in access control.

Step 3: Automate AI Red Teaming for Scalability

Manual testing alone cannot keep pace with large-scale or continuously updated AI deployments. Automation enables systematic coverage and regression tracking.

Popular tools include:

• garak: Opensource adversarial attack framework for securing large language models

• PyRIT (Python Risk Identification Tool): Allows simulation of model extraction and evasion attacks

• Microsoft Counterfit: A model security attack simulation platform

• Adversarial Robustness Toolbox (ART): A framework for simulating adversarial attacks and defenses in traditional machine learning

Automation in adversarial attacks enables organizations to test thousands of adversarial variants, identify regressions, and track progress by comparing to a fixed baseline over time.

Step 4: Implement Continuous AI Risk Monitoring and Response

AI red teaming is an ongoing practice. As models are retrained and deployed against new systems, risks evolve.

Continual mitigations involve:

• Monitoring for emerging risks via MITRE ATLAS and OWASP AI Top 10

• Adversarial testing through CI/CD stages

• Implementing automated risk scores for prioritization

• Tracking drift and anomalies in behavior in production

Constant testing makes sure resilience tracks with model improvements.

Step 5: Align AI Red Teaming with Governance and Compliance

Technical security metrics are only half the story. An AI red team must take into account the regulatory and ethical considerations as well. Some of the standards and regulations that are relevant are:

• NIST AI Risk Management Framework

• EU AI Act

• SOC 2

• GDPR and CCPA

Red teaming results must become part of enterprise risk management (ERM) initiatives. Elevate disclosures of gaps and vulnerabilities to governance bodies so decisions are made under the watchful eye of responsible AI principles. Requires teams working cross-functionally.

Security, data science, Legal, and Compliance need to come together to effect policy, technical controls, and tomorrow's continued assurances.

Final Thoughts on AI Red Teaming

AI systems introduce risks that traditional security testing was not designed to handle. Their probabilistic behavior, dependence on training data, and exposure to prompt-based interactions create new opportunities for attackers to manipulate outputs, extract information, or bypass security.

AI red teaming helps organizations identify these weaknesses before they are exploited. By testing how models respond to adversarial inputs, prompt manipulation, and data-related attacks, security teams can uncover risks that standard penetration testing may miss.

As AI adoption grows, red teaming should become a continuous practice rather than a one-time exercise. A structured process that combines human testing with automated monitoring helps organizations keep pace with changing models and emerging threats, ensuring AI systems remain reliable and secure over time.

FAQs on AI Red Teaming

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

Classical pen testing assesses networks, applications, and infrastructure for known vulnerabilities, misconfigurations, software bugs, and exposed endpoints. AI red teaming assesses the unique risks that arise around AI. It models adversarial exploits against models, prompts, training data, and inference pipelines: prompt injection, model evasion, data poisoning, or output manipulation, which are all areas that classic pen testing would not cover.

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

How often will depend on the level of risk involved in the organization or the system, and the impact of the AI on the organization. AI red teaming should be performed at least annually, and more frequently in the event of any large change to the model, retraining period, architecture, or new external integrations. The need for continuous testing increases if a system poses a higher risk.

What metrics determine the success of AI red team engagements?

Success is quantifiable by measurable results, the number and level of severity of exploited vulnerabilities, how quickly the breached system is remediated, fewer replicated findings, and quantifiable improvements in the robustness of the model and the detection/monitoring controls. In the long run, this is a success as more tightly integrated governance and better detection/monitoring controls.

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

Red teams practice by presenting models with challenging, adversarial, and poisoned inputs and evaluating model responses. This uncovers bias amplification, unsafe model outputs, and inconsistent modeling behaviors, and highlights model vulnerabilities so that they may be eliminated far in advance of deployment.

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

Governance defines limits, owns red team activities, and manages oversight. It ensures testing is prudent, legal, and business aligned. Governance robustly institutionalises red team findings into risk registers, incident response, and compliance functions to create resilience by embedding red team insights into ongoing AI risk management, not one-off assessments.

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