How to Use MITRE ATLAS to Assess Threats to an AI System

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base of adversary tactics, techniques, case studies, and mitigations for AI systems. You can use it to assess threats by mapping your system's attack surface to ATLAS tactics and techniques, reading the case studies that match your deployment, and turning the listed mitigations into concrete controls. It works best as a structured checklist rather than a one-time lookup: start from your system type, walk the tactics in order, and record which techniques apply and which mitigations you already have.

Step 1: Describe your system and its attack surface

Before browsing ATLAS, write down the facts that determine which entries are relevant:

• Model type and lifecycle stage — training, fine-tuning, or inference only.
• Data and model supply chain — pretrained weights, third-party datasets, plugins, or APIs you do not control.
• Deployment surface — chat interface, agent with tool access, embedded classifier, or RAG pipeline.
• Assets at risk — model integrity, training data, user data, downstream actions the system can take.

This list is what you match against ATLAS entries. A model you only call through an API has a different exposure than one you fine-tune on internal data.

Step 2: Walk the tactics to locate relevant techniques

ATLAS organizes adversary behavior into tactics (the adversary's goal at a stage of an attack) and techniques (how the goal is achieved). Read the tactics as an attack chain and, for each, ask whether your system is exposed:

1. Reconnaissance — can an adversary learn about your model, its training data, or its API behavior?
2. Resource development — could someone obtain or build the models, data, or tooling needed to attack you?
3. Initial access — how could an adversary get a foothold (compromised model artifacts, malicious plugins, poisoned data)?
4. ML model access — what does an attacker gain from querying or otherwise reaching the model?
5. Execution, persistence, and privilege escalation — can a compromised model or pipeline run code, persist, or gain broader access?
6. Defense evasion — could inputs be crafted to avoid detection or filters?
7. Discovery and collection — what could be extracted about the model, its data, or its users?
8. Exfiltration and impact — what is the worst realistic outcome (model theft, data leakage, manipulated outputs)?

For each tactic, note the techniques whose preconditions match your Step 1 list. Skip techniques that require access you do not expose.

Step 3: Use case studies to understand the attack chain

ATLAS case studies document real or demonstrated attacks against AI systems. Use them to move from an abstract technique to a concrete sequence:

• Find case studies whose target resembles your system (same modality, similar deployment, similar data sensitivity).
• Trace the chain from initial access to impact, and mark the step where your current controls would have intervened.
• Note the assumptions the case study makes — a demonstration may rely on access or resources your adversary would not have.

This is where assessment becomes specific: instead of "prompt injection is a risk," you get "an attacker who can submit text to our support chatbot could attempt to reach the tool-calling layer, and our only control is an input filter."

Step 4: Convert mitigations into controls and monitoring

Each technique entry lists mitigations. Turn them into an actionable table rather than a reading list:

For each gap, decide whether to prevent, detect, or accept the risk, and assign an owner. Mitigations that are detection-oriented (logging, monitoring, anomaly alerts) are often cheaper to add than prevention and give you evidence that controls work.

Step 5: Validate and keep it current

• Test the assumptions — for high-priority techniques, run a tabletop exercise or a controlled red-team test against a non-production copy.
• Re-check after changes — new model versions, new tools or plugins, and new data sources change your attack surface; revisit the mapping when they change.
• Track updates — ATLAS is a maintained knowledge base, so new techniques and case studies will appear; schedule a periodic review rather than treating the first pass as final.

Common pitfalls

• Treating ATLAS as a scanner. It is a structured knowledge base; the assessment work is the mapping you do, not the site itself.
• Copying every technique. Relevance depends on your exposure — an inference-only API does not inherit training-time risks.
• Stopping at technique names. The value is in the case studies (attack chains) and mitigations (controls), so read those, not just the list.
• Ignoring the human and pipeline layers. Many AI incidents start in data handling, credentials, or integrations rather than in the model itself.

Related questions

• What is MITRE ATLAS and what is it used for?
• How is the MITRE ATLAS knowledge base organized?
• How does MITRE ATLAS relate to MITRE ATT&CK?