November 20, 2025

Large Language Models (LLMs) are often marketed as revolutionary tools for static application security testing (SAST): instant bug hunters, tireless auditors, even replacements for human reviewers. The truth is more complicated. 

Leveraging AI to check AI seems to be all the rage. Given this trend, security practitioners need to step back and evaluate the quality and effectiveness of AI-powered strategies. When the stakes are security, the difference between fluency and accuracy matters.

So, let’s take a look at how LLMs detect code vulnerabilities and the advantages and disadvantages of AI-assisted code analysis. Then we’ll be prepared to answer the question:  Is AI good enough to leave humans out of the loop?

How Machine Learning Detects Vulnerabilities

LLMs appear to have superpowers when it comes to semantic reasoning capabilities. However, the capabilities of LLMs are driven by sophisticated pattern matching, which heavily relies on training data. In other words, LLMs don’t ‘understand’ code execution; they remix what they’ve seen before.

Let’s take a deeper look. 

When analyzing code, LLMs operate by predicting the most likely next tokens based on patterns they have seen during training. This means they are better at spotting familiar structures, common bugs, or code that resembles examples in their dataset. They are less reliable when reasoning through complex execution flows. They can surface potential policy violations when they align with known patterns, yet they may overlook subtle issues that require semantic understanding. 

Knowing this helps us to understand that an LLM that has been trained on generally clean code could be less likely to identify vulnerabilities when asked directly. Therefore, analysts must carefully craft prompts with an understanding of the model they’re using, the target code, and the desired outcomes. 

Advantages of AI-assisted Code Analysis

Studies and personal experience both indicate that there are considerable speed gains to be had by automating summary analysis of code using agentic AI code tools. It certainly offers a novel method of analyzing patterns to identify emerging security risks. By offloading mundane code searching and breaking down complexity into digestible chunks, humans can concentrate on the more important parts of the analysis.

Research indicates that, under very specific circumstances, an LLM can surpass traditional SAST in finding vulnerabilities. Perhaps this is simply because of the existence of more usable patterns. Other research demonstrates that combining LLMs with existing static analysis tools is promising; it can find more vulnerabilities while reducing false positives.

This evolution of SAST hinges on exploration of these benefits. However, some may find the current state of AI-powered SAST disappointing, especially those wanting to leverage semantic reasoning capabilities to uncover a higher number of quality findings.

AI Aspirations Meet Real-world Requirements

Part of the disappointment encountered in AI use comes from the limitations inherent to AI-driven static analysis. While LLMs can spot familiar patterns quickly, they often miss subtle or unconventional vulnerabilities that require deeper semantic understanding. They may generate alerts that sound plausible but are ultimately irrelevant, creating additional work for analysts. Hallucinations, such as inventing non-existent functions or packages, can further erode trust in the results. 

The people behind the code understand context and nuances that AI can’t grok. AI tools lack awareness of context-specific rules, regulatory requirements, and organizational practices, which can allow important issues to slip through. Finally, overreliance on automation risks reducing human vigilance, giving a false sense of security. 

These factors make it clear that AI-powered SAST, in its current form, delivers significant benefits. However, using it to replace human expertise and traditional analysis techniques could introduce significant security gaps.

Human Trust: The Biggest Risk

Humans are inherently inclined to trust AI systems, even when they are flawed. A study by UC Merced found that in simulated life-or-death scenarios, approximately two-thirds of participants allowed a robot to change their minds when it disagreed with them, despite being informed that the AI’s advice could be wrong.

This tendency to overtrust AI can be particularly dangerous in static code analysis, where AI-generated findings might be inaccurate or misleading. Developers may place undue confidence in AI outputs, overlooking potential vulnerabilities or misinterpreting results. To mitigate this risk, it’s crucial to maintain a healthy skepticism and incorporate human judgment into the analysis process. By doing so, we can ensure that AI tools serve as effective aids rather than becoming sources of false assurance.

Answering the Question: Is AI Enough?

Anyone who has tried running an LLM against code knows the experience: answers arrive instantly, explanations sound authoritative, and sometimes the results are right. Other times, they are confidently wrong. This gap between fluency and reliability is what makes the role of AI in static code analysis both promising and perilous.

In practice, I recommend that LLMs be treated as an enhancer, not a replacement, for static analysis. AI tools can highlight patterns and articulate findings in natural language, but their reliance on training data and tendency toward fluent (but sometimes incorrect) reasoning make them unreliable as a standalone detection engine. By understanding the benefits of speed and scale of AI, while simultaneously acknowledging its inherent pattern-matching mechanics and their limitations, developers and security analysis can set realistic expectations. With carefully crafted and well-trained LLMs, organizations can leverage AI as an intelligent assistant rather than a fully autonomous auditor.

So, to answer the question: AI is a powerful and useful tool, but it does not replace the need for human nuance and expert evaluation. 

Get the Full Story Behind AI-augmented Application Security

Understanding the strengths and limitations of AI-powered static analysis is crucial as the field rapidly advances. While AI tools can augment vulnerability detection, expert human operators remain essential to interpret results, validate findings, and maintain a security edge. 

For a deeper dive on how organizations can strike the right balance between AI-driven analysis and human insight in application security, read our whitepaper, Human Expertise Meets AI.