LLM Red Teaming: Finding Failures Before Attackers and Regulators Do

This online course provides practitioners with practical skills for identifying vulnerabilities in large language models through systematic red teaming approaches. Participants will learn to apply established attack taxonomies that categorize common failure modes including prompt injection techniques, jailbreak methods, and adversarial prompt strategies. The curriculum emphasizes hands-on experimentation with various testing methodologies that simulate real-world threats from malicious actors and regulatory bodies. Students will develop expertise in creating effective adversarial test cases while understanding how different model architectures respond to crafted inputs. The training covers essential techniques for automating repetitive testing processes and maintaining consistent evaluation standards across diverse applications.

The course structure focuses on practical implementation through guided exercises that mirror professional red teaming scenarios. Learners will practice scoring identified weaknesses according to standardized frameworks specified in relevant technical documentation. Reporting methodologies are taught to ensure findings communicate clearly to technical and non-technical stakeholders. Participants gain experience in documenting attack vectors, assessing impact severity, and recommending remediation strategies. The training addresses how to maintain testing consistency while adapting approaches to different model configurations and deployment environments. Students complete the program with confidence in conducting independent red teaming assessments that meet professional standards for identifying potential failures before they become critical issues.

Frequently asked questions

What is LLM red teaming?

LLM red teaming involves testing large language models by attempting to identify vulnerabilities, biases, or unsafe outputs through adversarial prompts and scenarios. The process typically follows ISO 27001 clause 8.2.3 for information security risk assessment and clause 11.2.1 for monitoring and measuring security controls. Red team exercises help organisations understand potential weaknesses in AI systems before deployment.

How is AI red teaming different from penetration testing?

AI red teaming involves simulating adversarial attacks on artificial intelligence systems to identify vulnerabilities in their design, training data, or decision-making processes. Penetration testing focuses on evaluating the security of computer networks, systems, or applications through authorised simulated attacks. While both activities aim to find weaknesses before malicious actors exploit them, AI red teaming specifically targets AI behaviours and outputs rather than traditional network or software vulnerabilities.

What is a jailbreak in a language model?

A jailbreak in a language model refers to techniques that bypass built-in safety measures and ethical constraints programmed into AI systems. These methods attempt to coerce models into generating content that would normally be blocked or restricted. Clause 7.2 of ISO 27001 addresses the management of information security risks including protection against unauthorized access or manipulation of systems.

What is indirect prompt injection?

Indirect prompt injection occurs when an attacker manipulates input to a system through intermediary steps rather than directly inserting malicious code into a prompt. This technique involves exploiting vulnerabilities in how systems process and handle data flows between different components or interfaces. The attack can bypass direct security measures by using legitimate system functions to gradually introduce harmful instructions.

How often should an AI system be red teamed?

An AI system should undergo red teaming periodically throughout its development lifecycle and deployment. The frequency depends on the system’s complexity and risk level but typically involves regular testing cycles. Organizations should conduct red team exercises whenever significant changes are made to the AI system or when new threats emerge.

Can AI red teaming be automated?

AI red teaming can incorporate automated tools to identify vulnerabilities and test systems at scale but cannot fully replace human judgment and creativity. Automated approaches work best when combined with human oversight to interpret results and develop effective countermeasures. The process requires careful design to ensure automated systems do not inadvertently create new weaknesses or miss subtle security issues.

Does the EU AI Act require adversarial testing?

The EU AI Act does not explicitly mandate adversarial testing as a requirement. Clause 5.1 of the Act sets out general risk management obligations for AI systems but does not specify particular testing methodologies. The legislation focuses on risk-based approaches where higher-risk AI systems must undergo conformity assessment procedures that may include various forms of testing but do not prescribe adversarial testing specifically.

How do you measure attack success rate?

Attack success rate measures the percentage of attempted cyber attacks that successfully compromise a system or network. Organizations calculate this by dividing the number of successful attacks by the total number of attack attempts over a specific period. Security teams track this metric to assess their defensive effectiveness and identify areas needing improvement.

Should AI red teaming be done internally or by an external team?

Organisations can choose between internal or external AI red teaming depending on their resources and expertise levels. Clause 8.2 of ISO 42001 specifies that organisations should identify and address AI risks through appropriate processes which may involve either internal staff or external specialists. External red teams often bring fresh perspectives and specialised knowledge but internal teams maintain better understanding of organisational context and existing systems.