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Choose an AI model API by checking the data terms, deployment route, access controls, usage limits, output detail, guardrail coverage, and incident response for the exact model and features you plan to use. Treat these as risk-reduction layers, not proof that a model is extraction-proof: the available studies examine particular prediction and NLP APIs, not a current comparative test of all commercial providers.
What model extraction is—and what it is not
Model extraction, also called model stealing, uses query access and observed input-output behavior to build a local model that approximates a target model. Each useful response can reveal information about how the API behaves, so a public query interface has to balance utility with the exposure of model behavior.
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Do not confuse extraction with two related risks. Prompt leakage is an attempt to reveal hidden instructions or configuration; AWS lists it as a type of prompt attack. LLMjacking is the use of stolen credentials to obtain or resell API access. These require overlapping but different defenses: reducing output detail may make extraction harder, while credential protection addresses unauthorized use. AWS Bedrock prompt-attack documentation and the Cloud Security Alliance note on LLM model extraction describe these issues in their respective scopes.
What the extraction studies do—and do not—show
Historical research demonstrates why query access deserves attention, but it does not establish that a particular API available today can be extracted at a particular rate.
#1 Best Overall
- In 2016, Tramèr and coauthors demonstrated attacks against the online services of BigML and Amazon Machine Learning. Their paper reports that withholding confidence values alone still permits potentially harmful extraction attacks. That is evidence against relying on confidence-value removal as a complete defense in the prediction APIs they studied—not a test of every current model API. Read the 2016 study.
- A 2019 study of BERT-based APIs found that the membership-classification and API-watermarking defenses it examined worked against naive adversaries but were ineffective against more sophisticated ones. Those results are specific to the studied APIs and methods. Read the 2019 study.
These findings support a layered selection and deployment strategy. They do not support a current ranking of providers by extraction resistance or a guarantee that any documented feature prevents determined extraction.
Compare the controls that affect your actual risk
Before comparing API brands, map the controls to your application. A setting documented for a provider’s direct API may not apply when a cloud platform processes the request, and a guardrail may cover only certain inputs or inference operations.
| Decision area | What to verify | Why it matters |
|---|---|---|
| Data retention and training use | Whether prompts, context, and outputs are retained; for what purposes and duration; whether reduced-retention options are available; and which features they exclude. | Data handling is a separate procurement question from whether query outputs could help approximate model behavior. |
| Processing route | Which organization processes the request on the selected route: the model provider or a cloud platform. Confirm that the terms you rely on apply to that route. | A provider’s direct-API terms may not govern a request handled through a partner platform. |
| Identity and access | Whether credentials can be scoped to an organization, project, workload, or end user; how secrets are stored and rotated; and how unusual access is investigated. | Scoped, monitored access helps limit unauthorized API use, including credential theft, and makes suspicious query patterns easier to investigate. |
| Rate, volume, and spend controls | Which account or workspace limits apply, whether spend caps and alerts are available, and whether a documented limit is a ceiling or guaranteed capacity. | Limits can constrain usage and manage cost, but public documentation of a quota does not establish that it stops a determined extraction campaign. |
| Output exposure | Whether users need confidence scores, detailed reasoning, or other information beyond the answer itself; whether the application can return less. | Removing unnecessary detail can reduce exposure, but it is a friction measure rather than a guarantee against extraction. |
| Guardrail coverage | Whether filtering covers user input, model output, retrieved content, tool calls, tool results, prompt attacks, or prompt leakage—and what tags or endpoint settings it requires. | A feature’s name does not establish what it inspects. Configuration and scope determine whether it addresses your threat. |
| Detection and response | What activity is monitored, who reviews flagged material, and what suspension, appeal, or support process applies. | A control is more useful when your team knows how to investigate and respond to an alert. |
| Application testing | Whether the deployed application has been tested for repeated queries, prompt injection, account sharing, and unexpected high-volume use. | API terms and provider controls do not replace testing the behavior of your own application. |
What the provider documentation says
The following points describe specific public documentation, not a ranking. Check the linked terms for your chosen model, feature, region, account, and processing route before launch; provider policies and limits can change.
OpenAI API
OpenAI says abuse-monitoring logs may contain prompts, responses, and derived metadata, and are retained for up to 30 days by default, subject to stated exceptions. Eligible organizations may seek approval for Zero Data Retention or Modified Abuse Monitoring; feature-level limitations apply. See OpenAI’s API data-controls documentation for the applicable terms and eligibility details.
Rank #3
For application design, OpenAI recommends adversarial testing, moderation, human oversight where appropriate, requiring registration and login in general, and limiting user-input and output-token volume. These are safety recommendations for building an application, not a claim that a specific control prevents model extraction. OpenAI API safety best practices.
Anthropic Claude API
Anthropic describes Zero Data Retention for eligible API features where Anthropic is the processor. Its documentation directs customers using Amazon Bedrock or Google Cloud to those cloud providers’ retention terms instead. Do not assume ZDR applies to every feature or deployment route; the cited page does not state one comparable retention duration for all eligible use. Anthropic API and data retention.
Anthropic documents service-configured organization-level rate limits, optional workspace-configured limits, usage tiers, and monthly spend caps. It cautions that documented limits are maximum allowed usage, not guaranteed minimums. Anthropic Claude API rate limits.
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Google’s Gemini API abuse-monitoring policy, last updated June 9, 2026 UTC, says its Trust and Safety team uses automated and manual processes. For the API and AI Studio scope described on that page, prompts, contextual information, and outputs may be retained for 55 days for abuse monitoring, safety, and required legal or regulatory disclosures; authorized personnel may review flagged content. Assess whether those terms suit the data you intend to send. Google Gemini API abuse-monitoring policy.
Best Value
Amazon Bedrock prompt-attack filtering
AWS documents prompt-attack filters for jailbreaks, prompt injection, and prompt leakage. For InvokeModel and InvokeModelWithResponseStream, user input must be tagged for prompt-attack filtering; AWS states, “If there are no tags, prompt attacks for those use cases will not be filtered.” The filter does not evaluate tool results or tool definitions. Its configuration includes detect-only or block actions and threshold settings, so confirm that your chosen operation and the content you want inspected are covered. Amazon Bedrock prompt-attack filtering.
Put the safeguards into practice before launch
- Define the asset and exposure. Decide what model behavior or proprietary capability you need to protect, what information the application must return, and which user groups can query it.
- Choose the processing route, then verify data terms. Record the exact model, API feature, cloud or provider route, and account. Check the applicable retention, training-use, and reduced-retention conditions rather than assuming a setting carries over between routes.
- Limit access and query volume. Scope credentials to the workload that needs them, store and rotate secrets securely, and configure the relevant rate, volume, and spend controls. Set monitoring and alert ownership so unusual use can be reviewed.
- Return only what the use case needs. Avoid exposing confidence values, detailed reasoning, or other output fields unless they are necessary. Treat reduced output as one friction layer, not a standalone extraction defense.
- Configure guardrails for their documented scope. Check which inputs or outputs are inspected, whether tags or endpoint settings are mandatory, and whether tool-related content is included. Test the behavior with the same inference route your application will use.
- Red-team the complete application. Test repeated and high-volume queries, prompt injection, account sharing, and unexpected access patterns. Add moderation and human review where appropriate, and establish how the team will investigate and respond to suspicious activity.
- Recheck before contracting and launch. Review the current provider documentation and account-specific eligibility for every selected feature; retain a record of the exact terms and controls your implementation depends on.
No cited public documentation establishes one API as safest overall across data handling, access, output exposure, guardrail coverage, and response. Choose based on the sensitivity of your data and the controls you can verify and operate on the exact deployment route.
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