No—not by itself. Encryption can protect model files, data, and communications from unauthorized access, but it cannot stop an authorized user from querying an AI service and learning from the answers it returns. Preventing that kind of model extraction requires controls at the API and output boundary, not just encrypted storage.
What does “model distillation” mean in this context?
People use “model distillation” to describe different things. In machine learning, knowledge distillation can mean deliberately training a smaller model from a teacher model’s outputs. In a security context, the related threat is often called model extraction or model stealing: an attacker sends queries to a model and uses the responses to learn about its behavior, architecture, or parameters.
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NIST defines model-extraction attacks as attempts to learn information about model architecture and parameters by submitting specially crafted queries. That means a person may learn useful information without ever accessing the stored weight files. NIST’s 2025 adversarial machine-learning taxonomy describes the threat in those terms.
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What encryption can protect
Encryption is valuable, but its protection depends on where it is applied and what access the attacker has.
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| Protection layer | What it helps protect | What it does not prevent |
|---|---|---|
| At rest | Model weights, training data, and backups stored on disks or other storage, if keys are protected separately. | An authorized API user learning from responses. Encryption does not hide the model’s answers from the people permitted to receive them. |
| In transit | Requests and responses against interception while moving between a client and service. | Analysis by the authorized client receiving the response, or misuse of valid account access. |
| During processing | Some exposure of data while cloud workloads process it, using confidential-computing techniques such as hardware-enabled isolation. | The service’s decision to return informative outputs to a caller. |
| At the output boundary | Not encryption: API authorization, output minimization, query limits, and monitoring can reduce or detect extraction attempts. | All risk. Effectiveness depends on implementation and how an attacker adapts. |
Ordinary computation generally requires information to be usable by the processor. NIST’s May 2026 initial public draft on confidential computing describes extending encryption to data in active use through hardware-enabled isolation. This can mitigate some infrastructure exposure, but it does not determine what answers an API should release. NIST IR 8320E is a draft, so its status may change.
Can someone learn a model through its API?
Yes, an API can expose signals useful for learning a model, even when its weights remain inaccessible. A 2024 ICML paper by Carlini and colleagues reported recovering an embedding projection layer from production language models through typical API access. The authors reported extracting the entire projection matrix for the Ada and Babbage models they studied for under $20, and estimated a query cost under $2,000 to recover the GPT-3.5-turbo projection matrix.
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Those figures apply to specific model components and the study’s conditions. They are not a general cost for stealing a model, and the paper did not demonstrate cloning an entire current frontier model. Its central lesson is narrower and still important: outputs can reveal structural information that storage encryption does not conceal. The ICML 2024 paper documents the experiment and its scope.
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Because the attack path is the model’s interface, defenses need to govern who can query it, what the service returns, and how suspicious activity is handled. OWASP’s living AI Security Verification Standard includes model-extraction defense requirements, but guidance is not evidence that a particular deployment is protected. OWASP AISVS is a verification resource, not a guarantee.
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- Control access: Require appropriate authentication and authorization, and apply limits to accounts, keys, and usage patterns.
- Watch query behavior: Look for suspicious volumes or patterns, including activity distributed across accounts, and have a response process for investigating and restricting it.
- Limit unnecessary output detail: Avoid exposing logits, probabilities, or other unusually information-rich outputs unless a legitimate use case requires them.
- Plan for adaptation: Rate limits and pattern detection can raise the cost of extraction and improve detection, but should not be described as making extraction impossible.
- Protect the infrastructure too: Encrypt stored assets and communications, manage keys carefully, and consider confidential computing for relevant infrastructure threats. These controls complement rather than replace interface defenses.
A September 2026 individual-authored Internet-Draft proposes an additional release-control architecture for sensitive, high-priority model information. It argues that authentication and confidential computing alone do not decide whether a pending release should be authorized. The draft is a proposal, not an adopted IETF standard, and does not claim universal prevention of extraction or distillation. The Internet-Draft should be read in that limited context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Do watermarks prevent model theft?
No watermark should be treated as an absolute barrier. Watermarks may help identify or attribute some outputs or copies after the fact, but they do not necessarily stop someone from extracting behavior through queries.
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A 2024 ICML study by Jovanović, Staab, and Vechev tested attacks that spoofed or scrubbed watermark signals. The authors reported an average success rate over 80% for the studied attacks, which cost under $50 in their experiments. That result is bounded to the watermark schemes and conditions tested; it does not establish that every watermark is vulnerable in the same way. It does show why watermarking is better treated as one possible attribution signal within a broader defense strategy. The study describes the tested schemes and results.
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Choose controls according to the asset and attacker you need to protect against, rather than treating all security technologies as interchangeable.
- Concerned about stolen storage or backups? Use encryption with sound key separation and access controls.
- Concerned about network interception? Protect communications in transit.
- Concerned about infrastructure exposure during computation? Evaluate confidential-computing approaches for the workload and threat model.
- Concerned about learning through a legitimate API? Focus on authorization, output choices, query monitoring, rate limits, and incident response.
- Need evidence of copying or attribution? Consider watermarking cautiously, and do not confuse a signal for attribution with prevention.
The most important distinction is between stealing model files and learning from model behavior. Encryption can make unauthorized access to files and data harder. It cannot, on its own, keep a model’s authorized responses from being analyzed.
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