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If you train ML models, they can learn to write more secure code. But the quality of the training data is only as good as your AppSec tooling.
As if human developers creating insecure code weren’t bad enough, now we have machine learning (ML) models doing it. Using large language models (LLMs) to write code is now a standard procedure in software development — despite the risk of creating flawed applications.
Using a benchmark it created for evaluating how well LLMs can create secure and correct backend code, the open-source BaxBench project found recently that 62% of the solutions generated by LLMs are either incorrect or contain a security vulnerability and that about half develop insecure code.
Rosario Mastrogiacomo, chief strategy officer for Sphere Technology Solutions, said LLMs can serve as a second set of eyes by reviewing their own output — and that, in turn, trains the model.
Rosario MastrogiacomoWhen given secure coding checklists or asked to critique code against known vulnerabilities, they can identify weaknesses and suggest fixes. This self-audit loop — generation followed by red-teaming with a new prompt — can meaningfully reduce risks, especially when paired with traditional static analysis tools.
Some prominent AI coding tools are now using feedback into the LLMs so that ML models can learn from their mistakes — and researchers say the potential is there for improvement. Here’s what you need to know.
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Why do AI coding tools spread security flaws?
Kurt Seifried, chief innovation officer at the not-for-profit Cloud Security Alliance (CSA), said that software teams have been trying to figure out how to write code securely for the past 50 years, but they haven’t figured out a reliable way yet. While tools such as memory safe languages can eliminate entire classes of vulnerabilities, things like logic bugs and poor architecture can still cause problems, he said.
Kurt SeifriedIt doesn’t matter how high the quality of steel and concrete is if you build the wrong type of bridge for a given place. LLMs are trained on data from humans, and even if the LLMs wrote perfectly bug-free code with no known security flaws, there’s a good chance that a new class or type of flaw will be discovered in a year or two or three that the code is vulnerable to.
Elango Senthilnathan, a solutions architect at Qwiet AI, said LLMs lack a deep understanding of security principles and often prioritize code that looks correct or common over what’s safe. They can miss context-specific risks, such as script injection or access-control flaws.
Roger Grimes, a defense evangelist at KnowBe4, said LLMs are basing the majority of the code they create on what people have written in the past, and that human-created code is full of security vulnerabilities.
Roger Grimes[The] creators of these code LLM models don’t prioritize doing secure coding over insecure coding. LLMs have no concept of that. They just code. So, if confronted with doing something in a secure or nonsecure way, the LLM isn’t weighted to do the secure [method].
Dwayne McDaniel, a developer advocate with GitGuardian, said LLMs are subject to the garbage-in/garbage-out problem. “That’s why they often suggest using hardcoded credentials or using vulnerable versions of a dependency,” McDaniel said.
Dwayne McDanielAt the same time, security threats are constantly evolving. Code that once might have been considered safe might suddenly have a zero-day discovered that affects the whole of the internet. LLMs are going to always struggle with how fast things change.
How ‘explained feedback’ can train AI to improve code security
One way to mitigate the security risks associated with LLMs is to have the models review the code they generate. Anthopic’s Claude Code and Microsoft’s GitHub Copilot are building out their tools to help developers protect code from security flaws.
A team of researchers at George Mason University found that a structured feedback loop is an effective way of addressing security flaws in AI code. “Our study revealed that while LLMs are inherently prone to generating insecure code, their security performance can be significantly improved through the incorporation of self-generated vulnerability hints and explained, contextualized feedback,” the researchers wrote.
The effectiveness of self-generated “hints” is contingent upon their relevance and preciseness, the researchers said. “Contextualized feedback can result in lower vulnerability rates in code repair tasks. Moreover, our comparative analysis across a diverse set of models highlighted that more advanced models tend to benefit more from the provided hints and feedback.”
The researchers emphasized the importance of “explained feedback,” where the output of a static analysis tool, CodeQL, is supplemented with contextual information and fed into the LLM so it can provide suggestions for code repair.
Abhay Bhargav, CEO and co-founder of Security Review AI, said it’s a good idea to add specific guidance in the prompts to reduce security flaws in the code they produce.
Abhay BhargavFor example, if your AI-assisted IDE is tasked with building a feature for a product, adding specific instructions on how the feature has to be secured from an authentication, authorization, input validation, and cryptographic perspective would be a better idea than just relying on vague statements like ‘build it securely.’
LLMs are heavily context-driven, Bhargav said. “Adding specific context makes a significant difference.”
Leveraging application security tool data is key
Mark Sherman, a technical director of Cybersecurity Foundations in the CERT division of Carnegie Mellon University’s Software Engineering Institute (SEI), said security teams should use the full collection of tools available to them, since LLM-created code is like any other code. “LLMs can augment conventional source code analysis [SCA] tools to investigate output of those tools, and conversely, the output of an LLM can be run through an SCA tool,” Sherman said.
Mark ShermanIn the most extreme case, one can alternate between an LLM and an SCA, having each one inspect and correct the other until one converges on code without any further diagnostics.
SEI software security engineer Will Klieber said the output of application security (AppSec) tools can be fed to LLMs, and often the LLM can use a tool’s output to identify and fix a security flaw.
But he warned that static-analysis tools sometimes report false alarms — and the LLM itself “can be used to adjudicate this.”
Iftach Ian Amit, founder and CEO of Gomboc.AI, said security and engineering teams need to use accurate and repeatable tools to assure security and reliability. That means that deterministic AI — which is rule-based and produces the same output for a given input, without randomness or surprises — must be part of the tool chain to complement and provide the right alignment for generative AI tools.
Iftach Ian AmitWhile generative AI can produce 10 times the code, it also generates 10 times the bugs, and without a scalable deterministic AI to address such bugs, any efficiency gains will be nullified.
The CSA’s Seifried recommended using multiple LLMs to secure code. “You can have one model write the code and a second, different model, audit and validate it,” he said. “Hopefully, this reduces the chance of an unknown security flaw slipping through because there is a lesser chance that both models will ignore it or don't know about it. Simply put, most everything we do with humans to try and reduce security fails in code is a good starting point with LLMs.”
Melody (MJ) Kaufmann, an author and instructor at O’Reilly Media, said security teams can harden LLM-created code by embedding checks directly into the CI/CD pipeline from static scans and infrastructure-as-code misconfiguration checks to secrets detection, so that insecure code is flagged before deployment.
Melody (MJ) KaufmannSome organizations are even experimenting with AI reviewers to augment human review.
Feedback is only as good as your AppSec tools
By conducting threat modeling and secure design reviews, security teams can also mitigate the risks associated with AI-generated code — and help train the models. “This will help define secure features that will, in turn, necessitate the AI-assisted integrated development environments [IDEs] to generate more secure code,” Security Review AI’s Bhargav said.
It also provides clearer security specifications that can be leveraged by AI-assisted IDEs to build more secure features consistent with the existing application architecture, Bhargav said
Security expectations should also be embedded into the developer workflow. “That means providing pre-approved prompt templates, secure coding playbooks, and automated checks that flag risky constructs immediately,” said Sphere Technology’s Mastrogiacomo
Rosario MastrogiacomoSecurity teams should act as enablers — giving developers clear guardrails and rapid feedback — rather than slowing them down with after-the-fact reviews.
Security teams need to understand how their developers are working and give them tools that can help them avoid checking in insecure code, said GitGuardian’s McDaniel. “Fortunately, the tooling market is fairly mature and new tools are emerging constantly,” McDaniel said.
One key area for advancement of the feedback into AI coding is to use advanced security practices such as complex binary analysis and reproducible builds. Saša Zdjelar, chief trust officer at ReversingLabs (RL), said the use of binary analysis and reproducible builds is a significant step forward over traditional static application security testing (SAST) and dynamic application security testing (DAST).
Saša ZdjelarAppSec practices such as SAST and DAST typically only apply to a small subset of internally developed systems and applications at many organizations.
In addition, CycloneDX’s machine learning bill of materials (ML-BOM) can provide rich feedback for training ML models. Dhaval Shah, senior director of product management at RL, wrote recently that developers and security teams need comprehensive visibility into their entire AI supply chain, and the ML-BOM is key to that.
Dhaval Shah[An] ML-BOM identifies potentially malicious open-source models before they can be integrated into your products, giving your development team confidence that the ML components they’re using are safe.
Security Review AI’s Bhargav said LLMs are our best possibility of writing secure code over the long term. “[But] it’s essential to understand that LLMs are only as good as the context they are given, and the more specific context in terms of security that you want to provide, the more the LLMs are able to generate more secure code,” he said.
Abhay BhargavLLMs are the best hope that we have to solve secure coding issues over the long term — but they have to be prompted and provided context in the right way.
