Can AI Reason About Code Security?

Author: Denis Avetisyan

A new study systematically probes the software security understanding of leading artificial intelligence models, revealing strengths in memorization but critical gaps in applying that knowledge.

The taxonomy categorizes prevalent software security misconceptions within large language models, illuminating systematic vulnerabilities arising from flawed understandings of core security principles and their application in these increasingly complex systems.

Research evaluates large language models’ comprehension of software security principles using Bloom’s Taxonomy, identifying consistent misconceptions and a clear knowledge boundary.

Despite the increasing integration of large language models (LLMs) into software development workflows, their underlying expertise in software security remains largely uncharacterized. This research, ‘Assessing the Software Security Comprehension of Large Language Models’, systematically evaluates the security knowledge of five leading LLMs across Bloom’s Taxonomy, revealing strong performance in recalling facts but a marked decline in higher-order reasoning tasks. Our analysis, utilizing diverse datasets and identifying a consistent ‘knowledge boundary’, demonstrates that LLMs exhibit predictable patterns of misconception even at basic cognitive levels. Will a deeper understanding of these limitations be sufficient to build truly secure software with the assistance of these powerful tools?

The Inevitable Erosion of Security: Adapting to a Shifting Landscape

Contemporary software security practices, largely built upon established principles of code review and penetration testing, are increasingly challenged by the sheer volume and sophistication of modern vulnerabilities. The escalating complexity of software systems, coupled with the rapid proliferation of interconnected devices and services, has broadened the attack surface and created opportunities for subtle, multifaceted exploits. Traditional methods often struggle to keep pace with this accelerating rate of change, frequently relying on reactive patching rather than proactive prevention. Consequently, security professionals face a growing backlog of potential weaknesses, while attackers benefit from an expanding landscape of undiscovered flaws, highlighting the urgent need for innovative approaches to vulnerability detection and mitigation.

The increasing sophistication of software vulnerabilities has created a pressing need for automated analysis, and Large Language Models (LLMs) are emerging as potentially transformative tools in this domain. While traditional methods struggle with the sheer volume and complexity of modern code, LLMs offer the promise of identifying and understanding vulnerabilities through natural language processing and code comprehension. However, this potential is tempered by inherent limitations; LLMs aren’t flawless code interpreters and can exhibit biases or gaps in knowledge. Effectively harnessing LLMs for vulnerability analysis requires careful consideration of both their capabilities – quickly scanning code and flagging potential issues – and their challenges, such as generating false positives or missing subtle flaws. The integration of LLMs, therefore, represents a dual-edged sword, demanding a balanced approach to leverage their strengths while mitigating their weaknesses within existing security workflows.

While Large Language Models (LLMs) demonstrate impressive capabilities in answering basic questions about software security – achieving an 0.89 ‘Pass@1’ score on multiple-choice questions sourced from educational platforms – their reasoning abilities falter when confronted with more nuanced challenges. This suggests a performance ceiling; LLMs can effectively recall and apply learned information in straightforward scenarios, but struggle with the complex problem-solving and contextual understanding required for robust vulnerability analysis. The discrepancy highlights an inherent limitation in current LLM architectures – a gap between superficial knowledge and genuine reasoning – which demands careful consideration as these models are increasingly integrated into security workflows. Effectively, LLMs excel at recognizing patterns but often lack the capacity to extrapolate those patterns to novel or complex situations, creating a potential blind spot for emerging threats.

Effective integration of Large Language Models (LLMs) into software security demands a clear understanding of their limitations. Research indicates that models like GPT-5-Mini exhibit a discernible knowledge boundary at the ‘Create Level’ – the capacity to generate novel solutions – with reliability consistently dropping as tasks require more original thought. Specifically, performance remains acceptable – maintained at thresholds of 0.6, 0.7, and 0.8 – only when the LLM applies existing knowledge; beyond this point, accuracy diminishes significantly. This suggests that while LLMs can assist in identifying known vulnerabilities and applying established fixes, relying on them for complex problem-solving or the creation of new security measures requires caution and human oversight to ensure dependable results.

BASKET is a framework leveraging Bloom’s Taxonomy to comprehensively evaluate software security knowledge.

Deconstructing Cognitive Capacity: Mapping LLM Performance to Bloom’s Taxonomy

Bloom’s Taxonomy, a hierarchical framework categorizing educational learning objectives, offers a systematic methodology for assessing the cognitive capabilities of Large Language Models (LLMs). The taxonomy defines six cognitive levels – Remembrance, Understanding, Application, Analysis, Evaluation, and Creation – each representing increasing complexity in cognitive skill. By framing LLM performance within these levels, researchers and developers can move beyond simple benchmark scores and pinpoint specific areas of strength and weakness. This structured approach allows for targeted improvements in LLM architecture and training data, focusing on enhancing abilities at levels where performance is lacking, and providing a standardized means of comparing different models’ cognitive aptitudes.

Large Language Models (LLMs) consistently exhibit high performance in tasks requiring factual recall and conceptual comprehension, as evidenced by achieving a mean score of 4.6 or higher on Introduction to Software Security quizzes. This capability stems from their training on massive datasets, enabling them to readily access and synthesize information. Specifically, LLMs demonstrate proficiency in identifying definitions, listing components, and explaining established concepts within the domain of software security. This strong performance at the ‘Remembrance’ and ‘Understanding’ levels of Bloom’s Taxonomy indicates a robust ability to process and reproduce existing knowledge, but does not necessarily translate to higher-order cognitive skills.

Performance metrics indicate a significant decrease in Large Language Model (LLM) capabilities when assessed at the ‘Analysis,’ ‘Evaluation,’ and ‘Creation’ levels of Bloom’s Taxonomy. While LLMs exhibit proficiency in recalling and understanding information, their ability to apply critical thinking skills-such as breaking down complex problems, justifying solutions, or generating novel approaches-is demonstrably limited. This suggests a reliance on pattern recognition and existing data rather than genuine reasoning, hindering performance on tasks requiring independent thought or the formulation of original content. Quantitative data reveals consistently lower scores on assessments designed to measure these higher-order cognitive functions, confirming the observed limitations in complex reasoning abilities.

Current large language models (LLMs) exhibit limitations in the domain of cybersecurity when applied to tasks requiring nuanced threat modeling and innovative vulnerability mitigation. Specifically, LLMs struggle to effectively deconstruct complex system interactions to identify potential attack vectors, and often fail to generate novel or contextually appropriate defensive strategies beyond established patterns. This deficiency stems from an inability to perform higher-order cognitive functions such as critical analysis of system designs, evaluation of trade-offs between security measures, and the creation of unique solutions to emerging threats; instead, LLMs tend to rely on memorized patterns and readily available information, hindering their effectiveness in proactive security scenarios and incident response requiring adaptability.

Model performance varies significantly with temperature, demonstrating a clear distinction between low and high temperature regimes.

Quantifying Cognitive Drift: Benchmarking LLM Performance on Security Tasks

The SALLM Dataset and XBOW Benchmark are specifically designed to assess Large Language Model (LLM) performance in security-related tasks, providing standardized resources for vulnerability identification and analysis. SALLM consists of a collection of vulnerability descriptions and corresponding code examples, enabling evaluation of an LLM’s ability to understand and explain security flaws. The XBOW Benchmark complements this by offering a diverse set of security challenges, ranging from basic code review to complex exploit generation, allowing for a more comprehensive assessment of LLM capabilities. Both resources facilitate quantitative comparisons between different LLMs and track improvements in their security reasoning abilities, ultimately supporting the development of more reliable and effective LLM-based security tools.

The ‘Pass@K’ metric addresses the inherent stochasticity of Large Language Model (LLM) outputs by evaluating the probability of generating at least one correct response within K attempts. Instead of judging an LLM solely on a single output, ‘Pass@K’ allows for multiple generations per input, then calculates the proportion of inputs for which at least one of the K generated responses is correct. This is particularly useful for tasks like vulnerability identification where LLMs may produce varying outputs even with the same prompt. The metric is calculated as the number of prompts for which at least one of the K responses is correct, divided by the total number of prompts. For example, ‘Pass@5’ indicates the proportion of questions answered correctly in at least one out of five attempts.

Evaluation of Large Language Models (LLMs) on security tasks demonstrates a capability to identify frequently encountered vulnerabilities, but performance diminishes when presented with more nuanced or complex security flaws. Specifically, the Gemini-2.5-Flash model achieved a ‘Pass@1’ score of 0.84 when evaluated on multiple-choice questions sourced from publicly available internet data. The ‘Pass@1’ metric indicates the proportion of questions answered correctly on the first attempt. While this score suggests proficiency with readily identifiable vulnerabilities, it also highlights a limitation in tackling more sophisticated security challenges that require deeper reasoning and analysis.

Current evaluations of Large Language Models (LLMs) on security tasks, utilizing benchmarks like SALLM and XBOW, reveal limitations in identifying complex vulnerabilities despite achieving relatively high scores on simpler assessments, such as the 0.84 ‘Pass@1’ score attained by Gemini-2.5-Flash on multiple-choice questions. This performance necessitates ongoing development and iterative refinement of LLM-based security tools; improvements must focus on enhancing the models’ ability to detect nuanced flaws and reduce false negatives. Continued benchmarking and the creation of more challenging datasets are crucial components of this process, allowing for accurate measurement of progress and identification of areas requiring further research and development in LLM-driven security applications.

Unveiling Systemic Flaws: Uncovering LLM Misconceptions in Software Security

A comprehensive misconception taxonomy was developed to categorize consistent errors in Large Language Model (LLM) reasoning concerning software security. This taxonomy identifies patterns in LLM failures, moving beyond anecdotal observations to a structured understanding of where LLMs consistently struggle. The framework allows for the systematic analysis of LLM outputs, revealing predictable shortcomings in areas such as vulnerability identification, secure coding principle application, and code semantics interpretation. This structured approach facilitates targeted improvements to LLM training data and model architectures, enabling developers to address specific weaknesses in LLM-powered security tools.

Analysis of Large Language Model (LLM) performance on software security tasks revealed 51 distinct misconceptions, categorized across six Bloom’s Taxonomy levels – Remember, Understand, Apply, Analyze, Evaluate, and Creation. These errors manifest as misinterpretations of code semantics, leading to incorrect assessments of functionality; a failure to identify and account for edge cases and boundary conditions; and consistent inability to apply established secure coding practices, such as input validation or proper error handling. The identified misconceptions are not random; they demonstrate systematic flaws in the LLM’s reasoning about code and security principles, impacting its reliability in tasks like vulnerability detection and code review.

Mitigating identified LLM misconceptions in software security necessitates a multi-faceted approach to model improvement. Targeted training involves curating datasets specifically designed to expose and correct prevalent errors in code analysis and security reasoning. Refinement of LLM architectures may include modifications to attention mechanisms or the incorporation of specialized modules for handling security-critical code patterns. Both training and architectural changes require rigorous evaluation against benchmark datasets that reflect the distribution of real-world vulnerabilities and secure coding practices. Furthermore, iterative feedback loops, incorporating expert review of LLM outputs, are crucial for validating improvements and identifying remaining weaknesses.

Acknowledging and systematically addressing the identified misconceptions in Large Language Models (LLMs) is crucial for developing dependable LLM-powered software security tools. Mitigation strategies include focused dataset curation to correct erroneous reasoning patterns, architectural refinements to improve semantic code analysis, and the implementation of validation mechanisms to detect and flag potentially flawed security assessments. Improved reliability in LLM outputs directly translates to more accurate vulnerability detection, more effective code review automation, and ultimately, a stronger security posture for software systems relying on these tools. Continuous monitoring and retraining are necessary to address evolving attack vectors and maintain the trustworthiness of LLM-driven security applications.

Toward a Resilient Future: A Holistic Cybersecurity Framework

The Cyber Security Body of Knowledge (CyBOK) represents a significant effort to consolidate and structure the often-fragmented field of cybersecurity. It functions as a living encyclopedia, detailing core knowledge areas – from cryptography and network security to human factors and legal considerations – essential for professionals and students alike. Unlike traditional, vendor-specific certifications, CyBOK aims for broad, principle-based understanding, emphasizing fundamental concepts over specific tools. This approach fosters adaptability, allowing practitioners to apply core knowledge to emerging threats and technologies, including the evolving landscape of artificial intelligence and machine learning. By providing a shared, comprehensive foundation, CyBOK facilitates standardized education, promotes consistent terminology, and ultimately enhances the overall maturity of the cybersecurity profession.

For large language models to genuinely enhance cybersecurity, their application requires strict adherence to foundational security principles. Simply deploying an LLM doesn’t guarantee improved protection; instead, these tools must integrate with established methodologies like threat modeling, where potential vulnerabilities are proactively identified and mitigated. Similarly, secure coding practices – emphasizing techniques to eliminate common software flaws – are essential when developing or utilizing LLM-driven security solutions. An LLM analyzing code, for instance, should be trained to recognize and flag violations of secure coding standards, rather than merely identifying syntactic errors. Without this alignment to proven security frameworks, LLM-based tools risk becoming another layer of complexity, potentially introducing new vulnerabilities or failing to address critical risks effectively.

The escalating complexity of modern cyber threats necessitates a synergy between large language model (LLM) capabilities and seasoned human expertise. While LLMs excel at automating repetitive tasks like vulnerability scanning and initial threat detection, they often lack the nuanced judgment required to interpret ambiguous situations or anticipate novel attack vectors. Consequently, effective cybersecurity relies on a collaborative model where LLMs augment, rather than replace, human analysts. This partnership allows security professionals to focus on higher-level strategic thinking, threat hunting, and incident response, leveraging LLM-generated insights to accelerate decision-making and improve overall security posture. The most robust systems will not simply detect threats, but understand them in context-a capability best achieved through the combined strengths of artificial and human intelligence.

The convergence of large language models and human insight promises a significant leap forward in software security. Rather than replacing human security professionals, these advanced AI tools are envisioned as collaborative partners, augmenting their abilities to identify vulnerabilities and respond to threats with greater speed and accuracy. This synergistic approach addresses the limitations of both independent methodologies; LLMs excel at pattern recognition and automated analysis, while human experts provide crucial contextual understanding, critical thinking, and the ability to navigate nuanced or unforeseen security challenges. Consequently, future software systems are anticipated to demonstrate enhanced resilience, capable of withstanding increasingly sophisticated attacks through a dynamic interplay between artificial and human intelligence, fostering a more robust and adaptable cybersecurity posture.

The study meticulously charts the knowledge boundaries of Large Language Models regarding software security, revealing a curious imbalance. While these models excel at rote memorization-demonstrating strong recall of facts-they falter when asked to apply that knowledge in complex scenarios. This echoes Marvin Minsky’s observation: “You can’t swing a stick without hitting a frame problem.” The ‘frame problem’-determining what remains true when an action is taken-parallels the LLM’s difficulty with higher-order reasoning; the models struggle to discern relevant security implications when presented with novel or nuanced situations, indicating a fragility in their understanding beyond surface-level knowledge. Architecture without history is fragile and ephemeral, and so too is security knowledge without the ability to contextualize and apply it.

The Horizon Recedes

The findings presented here illuminate a predictable asymmetry. These Large Language Models demonstrate a facility for retrieving codified knowledge – a performance akin to meticulous cataloging. Yet, the transition from recall to genuine comprehension, to the application of security principles in novel contexts, remains stubbornly elusive. Every failure is a signal from time, indicating the limits of pattern recognition when confronted with the unanticipated. The consistency with which these models exhibit specific misconceptions suggests not a lack of data, but a fundamental challenge in modeling causality – in discerning not just what happens, but why.

Future work must move beyond simply increasing the scale of these systems. Refactoring is a dialogue with the past; simply ingesting more historical data will not resolve inherent architectural limitations. The focus should shift toward developing mechanisms for self-assessment, for models to explicitly identify the boundaries of their knowledge and to signal uncertainty. A system that knows what it doesn’t know is, paradoxically, more trustworthy than one that confidently asserts falsehoods.

Ultimately, the pursuit of ‘intelligent’ systems is an exercise in understanding decay. No model will remain impervious to evolving threats or unforeseen vulnerabilities. The question, therefore, is not whether these systems will fail, but how gracefully they will age – how readily they can be adapted, revised, and ultimately, allowed to yield to the inevitable currents of time.

Original article: https://arxiv.org/pdf/2512.21238.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/