What is AI Red Teaming? Testing AI/LLM Vulnerabilities | Cobalt

As artificial intelligence has become integral to business and security operations, AI red teaming has become increasingly critical to protect applications from vulnerabilities.

By simulating vulnerabilities that attack LLM (large language model) and Gen AI apps like prompt injection, sensitive information disclosure, and supply chain, AI red teaming helps security teams uncover defense gaps before hackers can exploit them. Here’s an overview of what AI red teaming is, what it’s for, how it’s done, what challenges AI red teams face, and how red teaming is becoming a standard part of AI risk management.

Defining AI Red Teaming

AI red teaming is the practice of stress-testing AI models to uncover flaws, biases, and security weaknesses before attackers can exploit them. It’s a specialized form of red teaming—offensive security testing that simulates realistic attacks against the applications, networks, and underlying LLMs that power AI-enabled systems. AI red teaming targets vulnerabilities in AI, LLM, or Gen AI applications that attackers are likely to exploit.

AI red teams take a step-by-step approach to testing application vulnerabilities. Testing starts with reconnaissance to gather intelligence on target attack surfaces, defenses, and vulnerable points. Using this information, red teams probe systems to gain initial access and then escalate privileges to achieve objectives such as stealing data, poisoning model output, disrupting apps, or hijacking LLM resources (LLMjacking). Testing culminates in reports listing findings, prioritizing fixes, and recommending remediations. Initial tests may be followed up with further testing using red teaming or other offensive security methods to confirm remediations and mitigate emerging vulnerabilities.

How AI Red Teaming Differs from Traditional Red Teaming

Traditional red teaming differs from other types of offensive security tests, such as penetration testing (pentesting), in that it is conducted without advance notice from the attacking team (the red team) to the target’s security team (the blue team). However, this is not necessarily true of AI red teaming.

AI red teaming also differs from its traditional counterpart methodologically. AI red team tests use a modified methodology that adapts the conventional red teaming approach represented by MITRE’s ATT&CK framework, adding some tactics and techniques specific to AI contexts, as illustrated by MITRE’s ATLAS matrix. While both approaches follow a similar pathway from reconnaissance through initial access to escalation and exploitation, AI red teaming includes some unique elements.

Notably, AI red teaming adds tactics and techniques for gaining AI model access following the initial access stage. For instance, AI model inference API access exploits vulnerabilities in inference APIs. AI red teaming also uses methods for AI attack staging after intruders have attained freedom of lateral movement inside systems. For example, attackers may craft adversarial data to alter model behavior.  

Another distinction from traditional red team tests is that whereas conventional testing usually explores vulnerabilities typical of infrastructure, networks, AI red teams test for risks characteristic of AI and LLM apps, such as:

• Prompt injection: undesired behavior triggered by user prompts that violate system parameters
• Supply chain vulnerabilities: risks entering LLMs from compromised supply chain training data, models, or deployment platforms
• Data and model poisoning: vulnerabilities, back doors, or biases introduced by compromised pre-training, fine-tuning, or embedding data
• Improper output handling: insufficient validation, sanitization, and handling of LLM outputs passed downstream to other components and systems
• System prompt leakage: exposure of sensitive information through manipulation of system prompts or instructions
• Misinformation: security breaches, reputational damage, or legal liability caused by the generation of false or misleading output that appears genuine
• Exposed RAG-indexed targets: exposed data from retrieval-augmented generation knowledge bases and endpoints providing targets for data poisoning or manipulation
• Open application repository search vulnerabilities: searchable vulnerabilities in repositories such as Google Play and the iOS App Store
• Open AI vulnerability analysis exposure: discoverable vulnerabilities in publicly available research on AI models
• Public AI artifact exposure: sensitive technical information on machine learning artifacts discoverable in resources such as cloud storage, public-facing services, and software and data repositories
• Retrieval content poisoning: malicious content designed to be retrieved by users
• AI model evasion: adversarial data to keep AI from correctly identifying content, such as malware
• Public-facing application prompt infiltration: risk posed by potential injection of malicious prompts into public-facing applications for downstream ingestion
• AI model access vulnerabilities: risks of bad actors gaining access to AI models’ inference APIs, AI-enabled products or services, white-box full model access, or physical environment access
• AI agent tool invocation: risk of intruders using AI agents to invoke integrated tools
• AI agent context poisoning: manipulation of AI agent LLMs to influence responses or behavior

Red teaming AI seeks to uncover these types of vulnerabilities and establish model robustness to withstand attacks exploiting these vectors.

Core Objectives of AI Red Teaming

AI red teaming may aim to achieve various goals:

• Uncovering vulnerabilities: Red teaming probes AI, LLM, and Gen AI applications to test their exposure to attack methods likely to be exploited by hackers.
• Following up on fixes: Red team tests may follow up on previous red teaming or pentesting to ensure that recommended remediations have been implemented successfully.
• Testing model reliability: Red teams can verify that models consistently produce accurate, reliable, trustworthy results.
• Verifying model fairness: Red team tests can check whether models display biased output due to causes such as biases in training data, algorithmic design flaws, or poor sampling.
• Checking model safety: Red teaming can test whether model output conforms to ethical, legal, and industry-specific safety standards.
• Confirming compliance: Red teams may verify that AI apps are compliant with regulatory frameworks such as the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS), the General Data Protection Regulation (GDPR), or emerging AI standards.

A given red team test may encompass one or more of these goals. Red team objectives are defined during initial scoping to bring the testing strategy in alignment with desired outcomes.

Common AI Red Teaming Techniques and Scenarios

Red teams use a range of techniques to probe AI system vulnerabilities. Some of the most common methods include:

• Adversarial prompt testing: This technique tests how AI systems respond to malicious prompts. For example, a red team attacker might try to get an LLM model to ignore its guardrail parameters by asking it to play the role of a fictional character without constraints. Typical adversarial prompt techniques include directly or indirectly injecting prompts, jailbreaking systems to operate outside their guardrails, tricking models into leaking system prompts or proprietary instructions, and embedding harmful requests into one or more seemingly harmless requests.
  
  
• Model inversion: This attack method attempts to reverse engineer model training data by analyzing output. For example, a red team attack targeting a healthcare diagnostic system might repeatedly query about different combinations of symptoms in order to identify a specific patient’s health issues. Model inversion techniques typically involve using statistical methods or surrogate models to map outputs to inputs and then optimize training data reconstructions or attributes.
  
  
• Bias probing: This attack method tests whether AI systems display biased output. For example, a red team might test whether a credit card provider skews application approvals toward a specific demographic. Red teams probe for AI bias using statistical analysis to check for parity across different groups, using explainable AI (XAI) to analyze model decision-making procedures, applying bias detection tools and libraries, and monitoring and auditing model output.

Applying these types of techniques enables red teams to evaluate how an AI system behaves under attack or manipulation.

Who Performs AI Red Teaming

AI red teaming may be conducted by different types of testers assembled from both internal and external talent. Testing teams may be recruited from:

• Security researchers: cybersecurity experts who specialize in AI vulnerabilities.
• Ethical hackers: security researchers with AI hacking skills authorized to conduct simulated attacks.
• AI governance teams: Groups of testers who confirm compliance with internal policies and regulatory frameworks by coordinating with all relevant departments to confirm that a company's AI usage meets standards.

Red teaming may be performed by internal or external testers. Larger AI developers may have dedicated internal red teams to test models during development and deployment. Organizations that lack internal AI cybersecurity expertise or tools may outsource to security companies or red teaming as a service providers to tap into external talent and resources. Some red team providers may specialize in specific areas such as AI safety, bias, or compliance. Crowdsourcing is another method of recruiting red team input.

Cybersecurity specialists increasingly pursue specialized training to obtain certification in red teaming and AI red teaming. Specialists in related disciplines such as AI and LLM pentesting may also possess the qualifications needed to do red teaming effectively.

AI Red Teaming Benefits for Organizations

AI Red team tests provide a valuable range of benefits for organizations, ranging from improving security to protecting brand reputation. Some of the most important advantages of AI red teaming include:

• Pre-empting security emergencies: By proactively identifying and mitigating risks before attackers can exploit them, AI red teaming helps organizations prevent data theft, ransomware attacks, app disruptions, and other emergencies.
• Strengthening AI resilience: Red teaming optimizes and fortifies AI security posture to better withstand cybersecurity stresses.
  Supporting shift-left security strategies: Conducting red team tests helps teams integrate security into all phases of the AI development and deployment lifecycle, supporting shift-left initiatives.
• Improving AI output: Red team testing reduces AI and LLM bias and improves accuracy and precision, enhancing the quality of app output.
• Promoting regulatory compliance: Subjecting AI apps to red teaming tests helps ensure that security posture conforms to regulatory requirements.
• Protecting brand reputation: AI red teaming helps demonstrate commitment to privacy and security standards, building customer trust and stakeholder confidence.

These important benefits serve critical business needs vital to protecting company finances, assets, operations, and external relations, accounting for why AI red teaming is becoming an increasingly critical part of security.

AI Red Teaming Challenges and Limitations

AI red teaming is an emerging cybersecurity specialization that faces some challenges to reach maturity. Some of today’s most pressing issues include:

• Lack of standardization: Although resources such as the Open Web Application Security Project (OWASP) help track LLM and Gen AI risks and mitigations, there is currently no standard framework for AI red teaming, making it challenging to measure security across different applications.
• Talent gaps: Running AI red team tests requires a multi-disciplinary skill set encompassing AI, cybersecurity, business operations, and domain-specific knowledge, which can strain the in-house resources of organizations.
• Access barriers to proprietary AI models: Red teams typically begin with only external access to AI models, which can increase the challenge of testing proprietary AI models due to lack of insights into internal code and data.

While these challenges can be significant, the limitations they impose can often be overcome. For instance, tapping into the expertise of experienced AI red teaming services, such as red teaming as a service providers, can close talent gaps and afford access to experts familiar with emerging standards. Likewise, designating a contact with internal model access to coordinate between red and blue team members (purple teaming) can help overcome the limitations of access barriers.

The Future of AI Red Teaming

Just as AI technology is advancing to keep pace with growing demand for LLM and Gen AI applications and shifting attack strategies, AI red teaming is undergoing a dynamic state of flux. From a niche experiment within offensive security, AI red teaming is on a path toward standardization and formal inclusion in AI risk management framework requirements. Both international and US leadership have recently pushed for standardizing AI security testing requirements, including red teaming. Meanwhile, AI automation is changing how red teaming is done, prompt usage of advanced tools to help teams conduct probes more efficiently and simulate attacks at scale. These trends will promote tighter integration between security frameworks, regulatory requirements, threat intelligence, and red teams.

To keep up with the rapid changes in the AI and IT security landscape, visit the Cobalt learning center, where you can find more informative articles to help you learn the fundamentals of cybersecurity and strengthen your cyber defenses.