Security teams should test AI and LLM applications the same way they test other high-risk systems, by combining offensive review, abuse-case thinking, and continuous validation of exposed interfaces, prompt handling, and access boundaries. The goal is to find how an attacker could manipulate the system, not just whether the model answers correctly. PTaaS-style testing helps expose practical weaknesses before production users or data are affected.
What “real-world attack paths” means for AI and LLM testing
For AI and LLM applications, the test target is not just model quality, but the full path an attacker could use to change outputs, steal data, bypass guardrails, or pivot through connected systems. That means testing prompts, retrieval layers, plugins, APIs, connectors, sessions, and any identity or authorization boundary that shapes what the system can see or do.
The most useful mindset is adversarial. Security teams should ask how the application behaves when inputs are malformed, malicious, socially engineered, or chained across components. A good test case may never look like a normal user prompt, because the real issue is whether the system can be manipulated into unsafe action, unsafe disclosure, or unsafe delegation.
One practical way to structure that work is to map likely attack paths from input to impact, then test each step separately. Identity Attack Paths Guide is useful here because the same path-thinking that finds privilege chains in identity systems also helps expose where an AI workflow can be steered into broader access or data exposure.
Where AI and LLM attack paths usually emerge
Attack paths often start where the application trusts user-controlled content too much. Prompt injection, indirect prompt injection, tool abuse, retrieval poisoning, and unsafe connector behavior are common because they exploit the gap between what the model was meant to do and what the surrounding system will actually permit. The model may be “correct” in a narrow sense while the application is still exploitable.
Another recurring failure mode is exposed interface logic. Teams should inspect APIs, admin functions, hidden routes, file upload flows, message queues, and integrations that an attacker could call directly rather than through the chat UI. If the AI feature can trigger actions, the action boundary matters as much as the text boundary.
Exposed secrets and reusable credentials are also part of the path. LLM applications often fail when logs, prompts, browser storage, or support tooling leak API keys, session tokens, or connector credentials. Recent incidents such as DeepSeek database exposure 2025 and LLM Provider API Key Security and LLMjacking Guide show why teams need to test for secret exposure, credential reuse, and unauthorized use of model or platform access.
How to design a test that reflects attacker behaviour
Start by testing the system as an integrated attack surface, not as a single model endpoint. That means combining red-team style prompts, abuse cases, prompt chaining, tool-call abuse, and attempts to cross trust boundaries between users, tenants, or workflows. If the application includes retrieval, connectors, or agentic actions, test whether the model can be pushed to request data it should not see or actions it should not take.
Then validate the boundaries that make the application safe in production. Review whether authentication is enforced at every path, whether access checks happen before retrieval and action execution, and whether the application fails closed when confidence is low or policy context is missing. Permission-Aware RAG Guide is relevant when the main risk is over-sharing from retrieval, while Enterprise AI Copilot Security Guide helps when the practical issue is connector governance, oversharing, and agent behavior in enterprise environments.
When the system uses external tools, include tests that probe for misuse of those tools under adversarial instructions. A model that cannot be made to answer a harmful prompt may still be able to trigger a harmful workflow through a tool, plugin, or retrieval step. That is why the test should follow the full chain from prompt to downstream effect, not stop at model output.
Risk and Threat Considerations
AI and LLM systems are risky because the attack surface spans language, data access, and delegated action. A weakness in prompt handling, retrieval, connectors, or permissions can turn a harmless query into data exposure, unauthorized action, or wider compromise if the system trusts model output too much.
Failure mechanism: Attackers exploit the mismatch between intended policy and runtime behaviour, often by injecting instructions, abusing tools, or using exposed interfaces and credentials to make the system reveal data or perform actions outside the intended scope.
Impact: The result can be prompt-based data exfiltration, access to sensitive records, compromised integrations, malicious content generation, or a larger foothold in connected systems before defenders detect the abuse.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and MITRE ATLAS address the attack and risk surface, while NIST AI 600-1 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI 600-1 | Generative AI Profile | Covers pre-deployment testing and GenAI risk management for this exact subject. |
| Recommendation — Apply the GenAI profile to test prompt, retrieval, and output risks before launch. | ||
| OWASP Agentic AI Top 10 | ASI02 — Tool Misuse | AI attack paths often exploit tools and external actions rather than model output alone. |
| ASI03 — Identity & Privilege Abuse | Testing must cover whether AI workflows can exceed intended access or delegated authority. | |
| ASI06 — Memory & Context Poisoning | Prompt and context poisoning are core attack paths for LLM applications. | |
| Recommendation — Red-team tool calls and block unsafe action paths before production. Verify every privileged action is separately authorized and observable. Test whether untrusted context can alter decisions, memory, or downstream actions. | ||
| MITRE ATLAS | Adversarial ML Threat Framework | Provides adversarial techniques for model, prompt, and agent attack-path testing. |
| Recommendation — Map likely AI attack paths to adversarial techniques and exercise them in red-team tests. | ||
| NIST SP 800-53 Rev 5 | CA-8 — Penetration Testing | Directly supports pre-production adversarial testing of exposed AI interfaces and flows. |
| Recommendation — Use penetration testing to validate AI attack paths before go-live. | ||
Practitioner Guidance
What to verify: Confirm that every meaningful AI action is gated by an explicit access check, not by model intent alone. If the application can retrieve, send, write, or trigger anything external, test those paths with low-trust inputs and with the wrong user, wrong tenant, and wrong context.
Implementation sequence: Test the highest-risk flows first: public prompts, indirect prompt injection surfaces, retrieval sources, tool calls, connector permissions, and logs or telemetry that may store sensitive content. Then expand into chained abuse cases that combine multiple weak points, because many real attacks depend on sequence rather than a single flaw.
Practitioner takeaway: The most realistic AI security test is one that proves whether an attacker can move from language manipulation to unauthorized effect, because that is where LLM risk becomes operational.
Related resources from NHI Mgmt Group
- How should security teams test and harden agentic AI applications before they go into production?
- How should security teams test AI chatbots that connect to sensitive data before they go live?
- How should security teams govern semiautonomous AI agents before they go live?
- How should security teams use AI pentesting to test real attack paths?
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Reviewed and updated by the NHIMG editorial team on October 30, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org