AI Security Testing: Threats, Approaches, and Defenses in 2026

What is AI security testing?

AI security testing is a process that involves identifying and mitigating security vulnerabilities specific to AI systems, such as large language models (LLMs). AI testing goes beyond traditional security to include unique risks like prompt injection, adversarial attacks, data poisoning, and model stealing, using approaches like static and dynamic analysis similar to application security testing.

Key components in AI security testing:

• AI application testing: Ensures safe and predictable AI behavior under real-world usage conditions. This involves simulating user interactions to uncover prompt injection risks, unintended responses, and output manipulation. This includes crafting adversarial prompts, manipulating context, and probing for unsafe content generation.
• AI model testing: Helps verify that the AI behaves as expected under stress, maintains integrity against adversarial interference, and protects the confidentiality of training data. This includes both black-box and white-box techniques to evaluate robustness against adversarial examples, inferential attacks, and model extraction.
• AI infrastructure testing: Ensures the operational environment is hardened against attacks that can compromise model integrity or system availability. It covers risks like insecure APIs, supply chain attacks, resource abuse, and plugin misbehavior.
• AI data testing: Helps prevent training on corrupted or illegal data, reducing the chances of harmful or biased model behavior in production. It involves auditing datasets for toxic content, imbalanced distributions, unauthorized personal data, and hidden triggers.

Threat landscape: What can go wrong with AI systems

Adversarial attacks and robustness failures

Adversarial attacks exploit the sensitivity of AI models to small, often imperceptible input changes. Attackers can craft data that appears normal to humans but causes the model to make incorrect or harmful decisions. This type of attack highlights the fragility of many AI systems, particularly those based on deep neural networks, which can be tricked by subtle manipulations. These vulnerabilities affect image recognition, natural language processing, speech recognition, and reinforcement learning systems deployed in real-world scenarios.

Robustness failures go beyond intentional attacks; they also include errors or breakdowns when AI systems encounter unfamiliar or noisy data in production. A lack of robustness undermines the trustworthiness of AI decisions, exposing organizations to operational, security, and compliance risks. Security testing must include continuous evaluation of an AI system’s resilience to adversarial inputs and unexpected data variations, using both automated tools and human-led testing to identify weaknesses before deployment.

Data-related risks

Data is foundational to AI, but it is also a key point of vulnerability. Data poisoning attacks involve injecting malicious samples into a model’s training set, manipulating its behavior in ways that benefit an attacker or degrade system performance. Poorly curated or unvalidated datasets can amplify inherent biases, propagate inaccuracies, and expose sensitive or regulated information. These data-centric risks can be subtle, taking time to manifest in production as the model processes new and potentially corrupted inputs.

Beyond poisoning, privacy leakage is another data-related risk. Model inversion and membership inference attacks enable bad actors to extract training data or determine if specific records were included in the training set, threatening user privacy and violating regulatory requirements. AI security testing must rigorously audit data sourcing, cleansing, and labeling processes, including investigating data lineage and access controls to prevent both intentional and accidental exposures of sensitive or manipulated information.

Model-level vulnerabilities

AI models can exhibit vulnerabilities in their internal mechanisms, such as susceptibility to model extraction, where adversaries query the system to reverse engineer its parameters or architecture. Techniques like model stealing provide a pathway for attackers to duplicate proprietary models, undermining intellectual property and allowing for further abuses like targeted adversarial attacks. These attacks reduce the differentiation and defensibility of AI offerings, exposing businesses to both technical and commercial threats.

Other model-level weaknesses include incorrect handling of edge cases, overfitting to non-representative data, and brittle logic paths that can be triggered to bypass security controls. Unintended memorization of training data can lead to information leakage, while inadequate monitoring of feature importance allows attackers to focus manipulations on influential variables. Security testing should involve both black-box and white-box analysis to uncover and remediate these vulnerabilities, hardening models against sophisticated adversaries.

System/infrastructure and operational risks

AI systems depend on complex infrastructure, including APIs, hosting services, and interconnected microservices. Each interface and dependency introduces attack surfaces vulnerable to exploits such as API abuse, privilege escalation, and insecure integration points. Unsecured infrastructure may provide attackers with avenues for lateral movement, data exfiltration, or model manipulation, making holistic system security a critical component of AI risk management.

Operational risks arise from lapses in deployment procedures, misconfigured access controls, or lack of runtime monitoring. Changes in the production environment—as well as the integration of third-party tools and cloud services—can introduce new vulnerabilities over time. AI security testing must extend beyond the core model to encompass the system architecture, focusing on both secure deployment practices and operational resilience against ongoing threats in live environments.

Misuse or misuse by design

AI systems not only face malicious attacks but can also be misused due to design flaws or a lack of oversight. Misuse by design occurs when an AI model is inadvertently configured to perform actions that are harmful, unethical, or contrary to regulatory requirements. This can include automating biased decisions, generating harmful content, or enabling unauthorized surveillance, all resulting from insufficient governance and security controls during the development process.

Deliberate misuse is another challenge, where users intentionally bend the system’s capabilities to achieve unintended or prohibited outcomes. For example, attackers might trick models into leaking proprietary information or providing assistance for harmful activities. Security testing must account for these scenarios by simulating real-world misuse, reviewing design choices for abuse potential, and integrating ethical considerations into all phases of the AI development and deployment lifecycle.

4 approaches to AI security testing

Here are several common approaches to carrying out AI security testing.

1. AI penetration testing

AI penetration testing adapts classic pen-testing techniques to the specific challenges and architectures of machine learning systems. Testers act as adversaries, attempting to breach model logic, extract sensitive data, bypass input constraints, or escalate privileges using identified weaknesses. Tests may target web applications integrated with AI models, end-to-end pipelines, or standalone inference APIs, deploying a wide toolkit of exploits tailored to each context.

2. Red teaming for LLMs and agentic systems

Red teaming focuses on emulating advanced adversaries intent on probing large language models (LLMs) and agentic systems for systemic weaknesses. In this approach, dedicated teams simulate highly creative or persistent attackers, using both automated tools and manual exploration to break protective constraints, induce harmful outputs, or extract sensitive underlying data. Red team exercises are particularly important for generative AI models, which may be used in complex, unsupervised environments facing dynamic real-world threats.

3. Adversarial input testing

Adversarial input testing probes the resilience of AI models by generating specially crafted test cases that aim to trigger erroneous or unintended behaviors. Often, these inputs are only slightly different from valid real-world examples, yet cause significant degradation in model performance or accuracy. Automated tools, such as adversarial example generators, create perturbations in images, text, or structured data, systematically challenging the model’s decision boundaries and highlighting weaknesses in training or architecture.

4. API fuzzing for AI services

API fuzzing involves automatically sending malformed or semi-random data to AI-driven APIs to discover errors, crashes, or unanticipated behaviors that could indicate vulnerabilities. This technique applies both to public endpoints and internal service interfaces, focusing on uncovering flaws in request validation, authentication, and data parsing. For AI APIs, fuzzing can trigger code paths that expose sensitive model logic, leak information, or allow input that bypasses security checks.

What is the OWASP AI Testing Guide (AITG)?

The OWASP AI Testing Guide (AITG) is a framework and best-practices manual to help organizations systematically assess and secure AI systems. Developed by the Open Web Application Security Project (OWASP), the AITG offers structured methodologies for identifying and testing the unique attack surfaces presented by machine learning and artificial intelligence implementations. It builds on well-established application security practices but adapts them to the specific challenges presented by AI, such as adversarial robustness and data-driven abuses.

The guide covers every stage of the AI development lifecycle, offering practical templates and actionable checklists for secure design, threat modeling, penetration testing, and risk management. By following the AITG, organizations can align their security processes with international standards, leverage community-driven tooling, and ensure repeatable, auditable security assessments.

Download the OWASP AI Testing Guide free from the official website.

Key components of AI security testing (based on OWASP AITG)

Let’s review the main techniques involved in AI security testing according to the OWASP AI Testing Guide.

AI application testing

AI model testing

AI infrastructure testing

AI data testing

Best practices for effective AI security testing

1. Validate all data inputs and training sources

Ensuring the quality, provenance, and integrity of data inputs and training sources is crucial for reliable AI system behavior. Poor-quality or manipulated data can introduce bias or enable downstream attacks through poisoning or leakage. Comprehensive validation processes involve automated checks for anomalies, manual review of data labels and sources, and rigorous tracking of data lineage. These measures guard against both external attacks and internal process flaws that can undermine model security or ethical compliance.

Proactive data validation extends to monitoring live data feeds for unexpected inputs and regularly updating test datasets to reflect real-world shifts. Organizations should vet suppliers or third-party data brokers to ensure alignment with security and privacy requirements. Secure data management practices help maintain AI robustness, reducing the risk of manipulation from adversarial actors or accidental intake of incorrect or malicious information.

2. Implement continuous behavioral testing of models

Continuous behavioral testing involves regularly probing model outputs under diverse and evolving real-world scenarios to detect unintended, unsafe, or unexpected behavior. Automated monitoring tools can simulate edge cases, measure output consistency, and flag query patterns indicative of attacks or misuse. This ongoing testing helps to identify drift, performance degradation, or newly introduced vulnerabilities resulting from changes to models, data, or broader IT environments.

Integrating continuous behavioral assessments into the release and maintenance cycle ensures that models retain reliability and resilience as they encounter unfamiliar inputs or adapt to new user contexts. Security and QA teams should build comprehensive test suites that include adversarial and stress-testing patterns, with the flexibility to rapidly update scenarios as new threats emerge. This approach provides early warning and a lower-cost path to remediation before business or user impact occurs.

3. Apply defense-in-depth to AI workflows and tooling

A defense-in-depth strategy layers multiple, redundant controls throughout the AI workflow. This begins at data ingestion—where filtering, validation, and access controls block malformed or malicious inputs—and extends through model training, deployment, and runtime monitoring. Security should be embedded at each stage: encrypting sensitive data, isolating models in containers or virtual environments, and securing APIs with authentication, rate limiting, and anomaly detection.

Applying layered defenses makes it significantly harder for an attacker to compromise the system through a single weakness or failure. This approach helps organizations respond to the reality that no model or pipeline is perfectly secure by ensuring that breaches are limited in scope and that secondary controls will activate if primary measures are bypassed. Continuous audit and review ensure that each control functions as intended and remains effective against emerging tactics.

4. Enforce access controls and least privilege for AI components

Access controls are central to reducing the risk associated with AI system compromise or unauthorized use. Enforcing least privilege means each user, service, or process is granted only the minimum necessary access to perform its function—nothing more. This limits the blast radius of successful attacks, for example, by preventing model extraction or unauthorized modification if one service credential is leaked or abused.

Implementing role-based access, strong authentication for privileged actions, and thorough auditing of access logs helps to detect and prevent abuse. For sensitive training or inference operations, implementing just-in-time access and approval workflows can further minimize risk. By applying stringent access management, security teams can better protect proprietary models, sensitive training data, and integrity of system configurations, irrespective of where components are hosted or deployed.

5. Monitor for drift, anomalies, and unexpected model outputs

Ongoing monitoring is essential to identify model drift, operational anomalies, or unexpected outputs that signal problems or active attacks. Monitoring solutions should track both input distributions and model performance metrics, alerting when outputs move outside normal bounds. Early detection of drift allows teams to adjust retraining schedules, patch vulnerabilities, or trigger incident response before models are exploited or degrade to the point of causing business harm.

Monitoring should be complemented by automated and manual review processes, including periodic audits of model decisions and outcomes for fairness, ethics, and compliance. Logs and alerts must be actionable and integrated with broader security operations for timely escalation. Effective monitoring builds confidence that AI systems remain safe and predictable, even as environments change, user behavior shifts, or adversarial threats evolve.

Related content: Read our guide to AI security solutions.

Conclusion

AI security testing is no longer optional. As AI systems become more deeply embedded in products and workflows, the attack surface grows—and the stakes of getting security wrong grow with it. From adversarial inputs and data poisoning to prompt injection and model extraction, the threats are diverse, evolving, and often invisible until it’s too late.

A structured approach—grounded in frameworks like the OWASP AITG and supported by continuous testing, defense-in-depth, and rigorous access controls—gives organizations the foundation they need to deploy AI with confidence.

Mend AI is built to support that foundation. From automated discovery and risk assessment of AI components across your supply chain, to system prompt hardening and red teaming for threats like prompt injection, context leakage, and hallucinations, Mend AI helps teams identify and address AI-specific risks before they reach production.