The Attack Taxonomy: OWASP & MITRE ATLAS
Lesson 7: The Attack Taxonomy โ OWASP & MITRE ATLAS
Red teaming needs a shared language. Two reference frameworks have become the industry standard for talking about AI attacks: the OWASP Top 10 for LLM Applications (application-focused, threat-oriented) and MITRE ATLAS (a full adversarial ML knowledge base modeled on ATT&CK). Learn both and you'll never again describe an attack with vague hand-waving โ you'll name the technique, the tactic, and the mitigation.
OWASP Top 10 for LLM Applications
OWASP (the Open Worldwide Application Security Project) maintains a ranked list of the most critical LLM application vulnerabilities โ think "OWASP Top 10 for web apps," but for AI. The current version (2025 update) is the definitive cheat-sheet for what can go wrong in an LLM application:
| Rank | Category | One-line description |
|---|---|---|
| LLM01 | Prompt Injection | Attacker text overrides the model's instructions (Lesson 4). |
| LLM02 | Sensitive Information Disclosure | The model leaks confidential data โ other users' info, secrets in training data, system internals. |
| LLM03 | Supply Chain | Compromised model weights, datasets, plugins, or dependencies flow into your system. |
| LLM04 | Data and Model Poisoning | Malicious data corrupts training, fine-tuning, or retrieval corpora (Lesson 6). |
| LLM05 | Improper Output Handling | Model output is trusted as code, SQL, or commands โ enabling injection downstream. |
| LLM06 | Excessive Agency | The model has too many tools and permissions, and can be steered into abusing them. |
| LLM07 | System Prompt Leakage | Attacker extracts the system prompt or hidden instructions (a favorite recon step). |
| LLM08 | Vector and Embedding Weaknesses | RAG/vector databases manipulated via poisoned or adversarial embeddings. |
| LLM09 | Misinformation | The model produces false or misleading content that gets acted upon (overreliance). |
| LLM10 | Unbounded Consumption | Unlimited context/token usage โ cost denial-of-service, resource exhaustion. |
MITRE ATLAS: The ATT&CK for AI
Where OWASP gives you a ranked top-10, MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) gives you the complete map โ 14 tactics and dozens of techniques, structured exactly like MITRE's famous ATT&CK framework for cyber adversaries. Tactics include Reconnaissance, Resource Development, Initial Access, ML Attack Staging, Exfiltration, Impact, and more; techniques carry IDs (e.g., AML.T0018 โ prompt injection) and cite real-world case studies of each attack actually being used.
Why ATLAS beats ad-hoc note-taking: (1) IDs, not descriptions โ "we saw AML.T0018" is precise and searchable; (2) kill-chain thinking โ it forces you to map the full adversary journey, not just the final exploit; (3) bridge to classic security โ techniques map to ATT&CK, so an AI incident can be tracked in the same language as your SOC's other alerts; (4) real-world grounding โ each technique links to documented cases (Tay, poisoning incidents, extraction attacks), which is gold for writing red team reports that management takes seriously.
๐ง Knowledge Check
1. A chat assistant can be tricked into revealing its hidden system prompt. Which OWASP category is this?
2. What makes MITRE ATLAS different from a simple vulnerability list?
3. An attacker sends an email that, when summarized by an AI assistant, causes the assistant to exfiltrate the user's data. Which categories are involved?