AI-powered applications can improve the user experience and give your organization a competitive edge. Still, artificial intelligence isn’t foolproof, and companies need to take proactive steps to secure these systems.
AI application vulnerability scanning systematically examines your AI models and code for weaknesses that attackers could exploit. Because traditional cybersecurity tools aren’t equipped to detect AI-specific threats, these purpose-built scanners are essential for protecting your organization’s sensitive data.
In this article, we’ll explore how AI application vulnerability scanning works and the six essential metrics that can help improve AI security.
An AI vulnerability scanner is a security tool that’s specifically designed to identify weaknesses in artificial intelligence systems. Instead of looking for conventional issues (e.g., open ports or outdated libraries), these scanners target vulnerabilities unique to AI and ML pipelines, such as model extraction, data poisoning, prompt injection, adversarial inputs, and exposed model APIs.
AI vulnerability scanners run automated tests against models and the environments that run them. They simulate attacks, scan model code, and test model endpoints to detect vulnerabilities, weak spots, and training logic errors in ML pipelines and data preparation scripts. Some scanners will even evaluate model outputs to check for model behavior that can leak sensitive information or potentially result in undesirable outcomes when deployed into production.
Without this kind of testing, most organizations have no idea how exposed their AI systems actually are. Traditional security tools weren’t designed to handle machine learning models, large language models (LLMs), or data science pipelines. AI vulnerability scanners fill that gap, answering questions such as:
This shouldn’t be a one-time test, either. Like penetration testing for traditional (non-AI) applications, AI vulnerability scanning should be continuous, especially in systems where models are frequently updated or retrained on fresh data, as is the case with many generative AI systems.
AI vulnerability scanners use automated analysis, pattern detection, and threat intelligence to quickly uncover vulnerabilities. These tools allow you to patch issues before they cause real damage.
Scanners improve your security posture and reduce overall risk, but they create an overwhelming amount of data. So, which metrics matter most?
While all performance data has a role to play, these are the six most important metrics to look out for in AI application vulnerability scanning.
The Prompt Injection Detection Rate is the percentage of prompt injection attempts (direct or indirect) that your system detects and prevents. Prompt injection can manipulate model behavior by embedding malicious instructions or escaping context boundaries.
A high value indicates that input parsing and sanitization layers are effective. A low value indicates the potential to allow attackers to control model output or leak information.
The Jailbreak Success Rate represents how often adversarial prompts overcome your safety guardrails or content filters. Jailbreaks are crafted to trick models into producing forbidden outputs, like hate speech or disallowed instructions.
If the Jailbreak Success Rate is high, it indicates that your safety systems are failing under pressure. Testing should include both zero-shot jailbreaks and chaining techniques that evolve over time.
The Adversarial Robustness Score estimates the resistance of your model to evasion attacks, particularly those that use minor input changes to trigger large or incorrect output changes. For example, attackers may alter a question or phrasing to bypass classification filters.
Robustness is generally measured with tools that automate adversarial testing, such as Mindgard’s Offensive Security solution. A low Adversarial Robustness Score suggests that your model is easily fooled or misled.
Data Memorization Risk assesses whether sensitive training data (e.g., names, passwords, financial details) has been memorized in a way that allows it to be extracted with cleverly crafted queries. Membership inference, extraction testing, and other similar approaches simulate real-world attempts at data leakage.
If known inputs are leaking into the model responses, then the model poses a privacy risk. It’s also in violation of most data governance standards, so rapid remediation is critical.
Toxicity or Harmful Content Probability is a metric that tracks the rate at which a model produces toxic, biased, or otherwise unsafe output. This can be measured by using toxicity classifiers or third-party APIs to score each output.
Metrics can include a per-sample toxicity score from 0 to 1, or a percentage of all prompts that led to harmful output. If this number is high, the model’s outputs need retraining, filtering, or tighter generation constraints.
Scan Coverage is the percentage of your AI codebase that is scanned for vulnerabilities. This metric is crucial because even the most advanced AI vulnerability scanner can’t protect what it can’t examine.
Tracking scan coverage over time ensures that as new code, models, or integrations are added, they don’t slip through the cracks. Expanding your scanning process in step with system growth is the only way to maintain full visibility into potential risks.
When it comes to AI application vulnerability scanning, tracking the right metrics can mean the difference between catching a critical security flaw early and facing a costly breach later. These metrics give you clear, actionable insights into your AI system’s risk posture, helping you prioritize fixes and maintain compliance.
However, it can be challenging to track these metrics internally, especially if you’re routing all resources to AI application development.
Don’t slow down your progress—balance output and security with Mindgard’s Run-Time Artifact Scanning and Offensive Security solutions. Our AI-powered solutions scan, test, measure, and protect AI applications with precision and confidence. Book a Mindgard demo now.
AI application vulnerability scanning is the process of using specialized tools to detect, assess, and prioritize security flaws in AI systems, including their code, models, and integrations. The goal is to find vulnerabilities before attackers can exploit them.
It depends on your risk profile and how often you update your AI applications. For most organizations, running scans after every major update and scheduling regular automated scans—weekly or monthly—is best practice.
Yes. GDPR, HIPAA, and ISO require proactive security measures. Tracking the right metrics from AI application vulnerability scanning not only reduces risk but also helps demonstrate due diligence during audits.
