Create Proxy AI Model

What a proxy AI model is used for

A proxy AI model is a substitute system that approximates another model’s behaviour closely enough to probe outputs, test assumptions, or rehearse abuse paths without interacting with the target directly.

That makes it useful for security teams studying how decisions shift under different prompts, inputs, or edge cases, and for adversaries who want to learn a model’s boundaries before attempting transfer attacks or automation at scale.

How proxy models are built and why fidelity matters

Proxy models are usually created by collecting enough observations from the target to reproduce its response style, decision thresholds, or classification habits. The closer the proxy tracks the original system, the more useful it becomes for both validation and attack planning.

Fidelity is the central trade-off. A weak proxy may reveal little more than broad tendencies, while a high-fidelity proxy can expose operational patterns, guardrail gaps, or fragile assumptions in the target system. The MITRE ATLAS Guide is a useful reference for understanding how adversarial AI behaviours are organised and mapped to attack techniques.

Security implications for AI systems

Proxy models matter because they lower the cost of experimentation. An attacker can use one to test prompts, craft evasion content, or automate abuse while avoiding direct exposure to the defended system. Defenders can use the same pattern to validate controls, compare expected and observed behaviour, and check whether a model is drifting in ways that create new exposure.

They are especially relevant when outputs are sensitive to query volume, prompt structure, or hidden decision logic. A proxy can surface weak points in model alignment, policy enforcement, or endpoint handling long before a live attack becomes visible.

For a broader view of AI governance, inventory, and oversight controls around model use, the AI Governance Guide and the AI Security Posture Management (AI-SPM) Guide help connect model behaviour to oversight and exposure management.

Detection and defensive response

Because proxy activity often involves repeated probing, defenders should watch for unusual query concentration, systematic variation in prompt structure, and bursts of requests against sensitive or high-value endpoints. Those patterns can indicate model extraction, behavioural mapping, or attack rehearsal rather than normal user interaction.

Response usually depends on limiting what can be learned from a single interface. Rate limiting, tighter output controls, stronger telemetry, and endpoint segmentation all reduce the value of the observations an attacker can collect. The LLM Security Guide is a practical companion for endpoint limits, abuse patterns, and model-facing controls, while the AI Security Maturity Model helps place those controls in a broader programme.

After suspected abuse, review whether the proxy was used only for reconnaissance or whether it likely informed a later exploitation attempt, including transfer attacks, jailbreak development, or automated abuse against production services.

Risk and Threat Considerations

Proxy models create a realistic pathway for intelligence gathering against AI systems because they let an attacker approximate the target’s behaviour without triggering the same defensive signals as direct probing. That makes them useful for staging, tuning, and repeated testing before a live campaign.

Failure mechanism: The attacker uses a sufficiently similar substitute model to infer response patterns, discover stable weaknesses, and refine prompts or inputs until the attack transfers to the real system.

Impact: The target may face more effective abuse, faster exploitation, greater automation of malicious activity, and a harder-to-detect pre-attack learning phase.

Practitioner Guidance

Why practitioners should care: Proxy-model activity is not just a theoretical research exercise. It can be an early warning sign that an AI system is being profiled for abuse, so security teams should treat persistent behavioural probing as a meaningful signal rather than background noise.

What to watch for: Repeated near-duplicate prompts, query bursts, unusual parameter sweeps, and attempts to elicit boundary behaviour are common indicators that a proxy is being trained or validated. Those signals deserve review alongside model telemetry and application logs.

Practitioner takeaway: The best defence is to reduce what the proxy can learn from observation alone, then make sure your monitoring can distinguish ordinary experimentation from systematic attack preparation.

Related resources from NHI Mgmt Group

• Why do AI pipelines and model registries create governance risk?
• Why do AI model servers create NHI governance risk even when deployed locally?
• Why do generative and agentic AI create problems for traditional model risk management?
• Why do AI agents create new IAM risks even when the model output looks acceptable?