Artificial intelligence systems can introduce security risks that conventional application testing does not always uncover. Large language models can interpret ambiguous instructions, retrieve external information, call tools, and make decisions that change according to context. AI agents add another layer of complexity because they may interact with databases, APIs, code repositories, and business applications. A vulnerability that appears minor during development can therefore become much more serious when an AI system receives real data and meaningful permissions.
This is why security teams increasingly test AI systems adversarially before release. Rather than asking whether a model performs its intended task, these assessments ask how an attacker could manipulate it, bypass its safeguards, expose information, or cause an agent to take an unauthorized action. NIST defines adversarial AI testing as a structured, often adversarial effort to identify flaws, vulnerabilities, unexpected behaviors, and misuse risks in AI systems.
Finding Weaknesses Through Adversarial Adversarial AI testing
Adversarial AI testing approaches an AI application from an attacker’s perspective. Testers deliberately construct inputs and scenarios designed to make the system violate its intended behavior. This can reveal weaknesses that ordinary functional testing misses because the goal is not simply to confirm that the application works—it is to discover how it can fail.
For example, testers may attempt direct prompt injection by instructing an AI assistant to disregard its system rules. They can also use indirect prompt injection by placing malicious instructions inside documents, webpages, emails, or retrieved content that the application processes. OWASP identifies these attacks as capable of manipulating model behavior, exposing sensitive information, bypassing safeguards, and influencing agent actions.
The testing can extend beyond prompts. Security teams may examine whether an AI agent can misuse tools, access information outside its intended permissions, manipulate memory, or chain several seemingly harmless actions into a harmful outcome. OWASP’s Genadversarial AI testing Guide recommends a holistic approach covering model evaluation, implementation testing, infrastructure assessment, and runtime behavior analysis.
This matters before production because vulnerabilities are generally easier and safer to correct when an application is still being developed. A failed adversarial test can become a development ticket, a new guardrail, a permission change, or a regression test rather than a production incident.
Testing Models, Applications, and Agents in Realistic Scenarios
A useful security assessment should reflect the environment in which an AI system will actually operate. Testing only a foundation model in isolation may overlook weaknesses introduced by retrieval systems, APIs, authentication, plugins, databases, or custom application logic.
For AI agents, this distinction is particularly important. An agent might be instructed to summarize customer information, retrieve a record, update a database, or execute a workflow. Each capability creates an additional attack surface. An attacker does not necessarily need to make the model produce harmful text; manipulating the agent into performing an unauthorized action may be enough.
Effective AI red teaming should evaluate not only the underlying model but also the applications, agents, tools, and data flows surrounding it. Realistic testing can examine whether prompt injection influences trusted instructions, whether an agent can misuse connected tools, whether retrieval or memory can be manipulated, and whether authentication or authorization weaknesses allow actions beyond the intended scope. Testing these interactions as complete attack paths provides a clearer view of how an AI system could be exploited in practice.
A practical pre-production assessment should consider at least these areas:
The objective is not to prove that an AI system can never be attacked. Instead, the objective is to understand realistic failure modes, determine their potential business impact, and establish controls that reduce the likelihood or consequences of successful attacks.
Turning Red-Team Findings Into Pre-Production Security Controls
The greatest value of AI red teaming comes from what happens after an attack succeeds. A useful finding should explain the vulnerable behavior, reproduce the conditions that caused it, identify the affected component, and provide enough evidence for engineers and security teams to remediate the problem.
Suppose a test demonstrates that an agent follows instructions hidden inside a retrieved document. The appropriate response may involve separating trusted instructions from untrusted content, restricting the agent’s tool permissions, adding confirmation requirements for sensitive actions, and introducing a regression test for the discovered attack. The same principle applies to data leakage, unsafe tool calls, excessive permissions, and other weaknesses.
Continuous testing is especially important because AI systems change frequently. A model update, prompt modification, new retrieval source, additional tool, or altered authorization policy can change the application’s security characteristics. Noma Security’s red-teaming documentation similarly emphasizes that prompt updates, model changes, and new tool integrations can alter an AI application’s behavior and attack surface.
This makes adversarial testing most effective when integrated into the development lifecycle rather than treated as a one-time security exercise. OWASP’s current AI Agent Security guidance recommends adversarial test suites in CI/CD, regression testing for previously identified failures, and release gates for significant changes to tool policies, approval logic, or credential scopes.
Why Continuous Testing Matters as AI Systems Evolve
Pre-production testing establishes an important security baseline, but it cannot guarantee that an AI application will remain secure indefinitely. Modern AI applications are dynamic systems. Models can be replaced, prompts can evolve, retrieval indexes can change, and new integrations can introduce previously nonexistent attack paths.
For that reason, organizations should treat security findings as an evolving body of knowledge. Every successful attack should inform future testing, while previously fixed vulnerabilities should become regression cases. This approach helps determine whether a security improvement remains effective after later engineering changes.
It is also important to distinguish model capability from security resilience. A highly capable model is not automatically a secure model, and additional reasoning or functionality can sometimes create new opportunities for manipulation. NIST’s adversarial machine-learning taxonomy recognizes multiple classes of attacks against generative AI, including evasion, poisoning, privacy, and misuse attacks.
For agents, the assessment should also examine business consequences. An innocuous-looking prompt may have limited impact in a chatbot with no external permissions, while the same manipulation could become serious when an agent can send messages, modify records, execute code, or access confidential information. Security testing must therefore evaluate not only whether an attack succeeds, but what the system can actually do when it succeeds.
Building Security Into the AI Release Process
The strongest pre-production programs connect adversarial testing with engineering, security, and governance processes. Findings should have clear ownership, severity ratings, remediation requirements, and verification steps. High-impact weaknesses should prevent release until appropriate controls are implemented or the residual risk has been explicitly accepted.
This process also benefits from maintaining evidence of what was tested. Teams should record the model or agent version, relevant configurations, attack scenarios, observed behavior, remediation decisions, and subsequent validation results. Such evidence makes security reviews more repeatable and provides useful context when the system changes.
The result is a security feedback loop rather than a single assessment. Developers learn how the application fails, security teams gain visibility into realistic attack paths, and governance teams gain evidence for risk decisions. Over time, the organization can build a growing library of adversarial scenarios tailored to its own AI environment.
End Note
AI systems should be evaluated according to how they behave under pressure, not only how well they perform normal tasks. Adversarial testing exposes weaknesses in prompts, models, integrations, permissions, retrieval pipelines, memory, and agent workflows before those weaknesses become costly production problems. When testing is repeated as systems evolve and successful attacks become regression cases, organizations gain a practical way to make AI deployments more resilient.
The goal is not to eliminate every possible failure. It is to discover important vulnerabilities early, understand their consequences, and strengthen the system before attackers have the opportunity to find the same weaknesses in a live environment.
