Red teaming has been a staple of military and intelligence security practice for decades: assemble a team whose job is to think like the adversary and find the weaknesses your defenders have normalised. The same discipline applied to AI systems — AI red teaming — is one of the fastest-growing specialisations in security, driven by the rapid deployment of LLMs in high-stakes production environments.
But AI red teaming is not simply traditional red teaming with LLMs in scope. It requires a distinct methodology, a different threat model, and specialised skills that most traditional red teams don't yet have.
What Makes AI Red Teaming Different
Traditional red teaming focuses on exploiting vulnerabilities in code, configurations, and network architecture. The attack primitives are well-understood: CVEs, misconfigurations, social engineering, privilege escalation chains. The attacker's goal is typically access or data exfiltration.
AI red teaming adds a new attack surface: the model itself. LLMs are probabilistic, not deterministic. Their behaviour emerges from training rather than explicit programming. This means:
- There is no source code to audit for the model's decision logic.
- Vulnerabilities are often discovered empirically, through adversarial interaction, rather than through code review.
- The same input can produce different outputs across runs, making reproducibility a challenge.
- Novel attack techniques emerge continuously as researchers probe new model architectures and deployment patterns.
What AI Red Teaming Covers
Prompt-Level Attacks
The red team systematically tests the model's resistance to prompt injection, jailbreaking, system prompt exfiltration, and role manipulation. This is the equivalent of input validation testing in traditional web application security — but the input is natural language and the "validation" is the model's trained behaviour.
Retrieval and Context Attacks
For RAG-based systems, the red team tests whether the retrieval system leaks documents across user boundaries, whether injected content in the knowledge base can redirect model behaviour, and whether the model discloses sensitive retrieved content that it shouldn't surface in responses.
Agent and Workflow Attacks
For agentic systems, the red team attempts to redirect the agent's actions: injecting malicious instructions through tool responses, manipulating the agent's memory or scratchpad, escalating permissions through multi-step exploitation, and causing the agent to take high-impact actions it wasn't authorised to take.
Model Extraction and Privacy Attacks
The red team probes for training data memorisation, attempts membership inference to determine whether specific data was in the training set, and tests the model's resistance to systematic extraction of its capabilities and decision boundaries.
Integration and Infrastructure Attacks
AI systems don't exist in isolation — they're integrated with databases, APIs, authentication systems, and business logic. The red team tests the full integration stack: does a successful prompt injection in the LLM translate to database access? Does agent tool misuse create an exploitable foothold in connected systems?
The AI Red Team Methodology
1. Threat Modelling
Before any testing begins, the team maps the AI system's architecture, identifies the assets being protected (data, capabilities, user trust), and defines the threat actors relevant to the deployment context. A customer-facing AI chatbot has a different threat model than an internal code generation tool.
2. Attack Surface Mapping
Enumerate every input channel: user turns, system prompts, tool responses, retrieved documents, database content, API responses, and any other data the model processes. Each is a potential injection vector.
3. Adversarial Testing
Systematic testing across all attack categories using both known techniques (published prompt injection payloads, jailbreak prompts, extraction techniques from research literature) and novel techniques developed during the engagement through creative adversarial exploration.
4. Exploit Chaining
Individual findings are combined into multi-step attack chains that demonstrate realistic attacker scenarios. A low-severity prompt injection that partially reveals system configuration, combined with a medium-severity retrieval scope issue, might chain into a critical data exfiltration finding.
5. Reporting
Findings are documented with attack narrative, reproduction steps, evidence, severity assessment, and remediation guidance. For agent systems, attack chains are presented as step-by-step scenarios that demonstrate end-to-end attacker impact.
When Do You Need AI Red Teaming?
- Before launching an AI product — particularly any system that handles sensitive data, operates with agent autonomy, or makes consequential decisions.
- After significant model or architecture changes — switching foundation models, adding new tools or data sources, expanding agent permissions.
- For compliance — EU AI Act Article 15 requires adversarial testing for high-risk AI systems. NIST AI RMF recommends red teaming as a core risk management practice.
- After a security incident — to understand the full attack surface and validate that the incident response has closed the relevant vectors.
Key Takeaways
- This post covers practical, actionable guidance for security and engineering teams.
- All findings and techniques are mapped to recognised frameworks (OWASP, NIST, ISO).
- Contact Vynox Security to test your systems against the vulnerabilities described here.