Building a Better Bar for Code Security

Author: Denis Avetisyan

A new automated pipeline dramatically reduces the effort needed to create challenging benchmarks for evaluating the security of code generated by large language models.

AutoBaxBuilder leverages LLMs to automatically generate code security benchmarks, improving functional correctness assessments and vulnerability exploitation testing.

Despite the increasing reliance on large language models (LLMs) for code generation, robust and evolving benchmarks to assess their security are critically lacking. This work introduces AutoBaxBuilder: Bootstrapping Code Security Benchmarking, a novel framework that automatically generates code security tasks and tests, circumventing the limitations of manually-crafted benchmarks prone to data contamination and unable to keep pace with LLM advancements. By leveraging LLMs themselves for functionality testing and exploit generation, AutoBaxBuilder creates challenging evaluations at a significantly reduced cost and timescale-generating new tasks in under two hours for less than USD 10. Will this automated bootstrapping of security benchmarks enable a more proactive and adaptable approach to safeguarding LLM-generated code against emerging vulnerabilities?

The Expanding Threat Surface of Automated Code

The proliferation of automatically generated code is fundamentally reshaping the software security landscape, dramatically expanding the potential attack surface. Modern development practices increasingly leverage techniques like low-code/no-code platforms, automated API generation, and code synthesis tools to accelerate delivery and reduce manual effort. While boosting productivity, this reliance introduces a new class of vulnerabilities, often stemming from insecure configurations within the generation process itself or from the inherent complexities of the generated code. These automatically produced components frequently lack the rigorous scrutiny applied to manually written code, creating blind spots for traditional security assessments. Consequently, developers must shift towards security testing methods specifically designed to address the unique challenges posed by dynamically created codebases, ensuring that automation doesn’t inadvertently introduce systemic weaknesses into software systems.

The accelerating pace of modern software development, driven by automated code generation, presents a significant challenge to traditional security assessment methodologies. These established techniques, often reliant on manual review or static analysis tailored to human-written code, struggle to effectively evaluate the sheer volume and intricacy of automatically produced codebases. The dynamic nature of generated code, frequently incorporating complex logic and diverse dependencies, introduces a level of obfuscation that bypasses conventional vulnerability detection. Consequently, security teams face a widening gap between development velocity and the ability to thoroughly assess risk, leading to potentially exploitable flaws slipping through the testing process and increasing the overall attack surface of deployed applications. This necessitates the adoption of more adaptive and automated security testing strategies capable of keeping pace with the demands of continuous integration and continuous delivery pipelines.

Despite decades of research and mitigation strategies, vulnerabilities like Cross-Site Scripting (XSS), SQL Injection, and Path Traversal continue to plague modern software applications. This persistence isn’t necessarily due to novel attack vectors, but rather a significant gap in the efficacy of automated detection tools. Current static and dynamic analysis techniques often struggle to identify these flaws within the rapidly expanding and increasingly complex codebases characteristic of today’s software development lifecycle. Many automated systems rely on pattern matching or signature-based detection, proving ineffective against even slightly obfuscated or context-dependent variations of these common attacks. Consequently, these vulnerabilities frequently bypass initial security checks and are exploited in the wild, demonstrating a critical need for more sophisticated and adaptable automated security testing capable of understanding code semantics and application behavior.

The enduring relevance of the Common Weakness Enumeration (CWE) underscores a critical gap in contemporary software security practices. This community-developed list of software and hardware weakness types isn’t merely a catalog of past mistakes; it serves as a persistent reminder that fundamental vulnerabilities – such as improper input validation, buffer overflows, and injection flaws – continue to plague modern applications. Despite advancements in development methodologies and security tools, the CWE demonstrates that these weaknesses are not being systematically addressed at scale. Consequently, a robust and scalable approach to security testing – one that moves beyond reactive vulnerability patching and embraces proactive weakness mitigation throughout the software development lifecycle – remains essential to effectively reduce the attack surface and improve the overall resilience of software systems.

Automated Security Benchmarking: A New Approach

AUTOBAXBUILDER is a novel framework leveraging Large Language Models (LLM) to automate the creation of security testing tasks and benchmarks. Unlike traditional methods that rely on manual design by security experts, AUTOBAXBUILDER generates security scenarios from inception, including the definition of objectives, inputs, and expected outputs. This LLM-based approach enables the rapid prototyping and deployment of diverse and challenging security tests without requiring extensive human effort in their initial design and specification. The framework’s core functionality centers on utilizing LLMs to synthesize both the tasks themselves and the associated test infrastructure, providing a fully automated pipeline for security assessment.

AUTOBAXBUILDER employs LLM Orchestration to manage the sequential and coordinated execution of multiple Large Language Models (LLMs) in the generation of security scenarios. This process involves defining a workflow where each LLM performs a specific task – such as defining attack surfaces, generating payloads, or crafting exploit logic – and then passing the output to subsequent LLMs. Orchestration ensures that these tasks are executed in a logical order, with dependencies handled automatically, enabling the creation of complex, multi-stage security tests that would be difficult to construct manually. The system dynamically adjusts LLM parameters and prompts based on intermediate results, promoting diversity in generated scenarios and increasing the likelihood of uncovering novel vulnerabilities.

AUTOBAXBUILDER’s assessment pipeline combines exploit generation and functional test generation to provide a holistic security evaluation. Exploit generation focuses on creating functional exploits targeting identified vulnerabilities, demonstrating real-world attack potential. Simultaneously, functional test generation creates tests to validate system behavior under various conditions, ensuring expected functionality remains intact despite security mitigations. This integrated approach allows for the verification of both vulnerability existence and the effectiveness of defensive measures, providing a more complete and reliable security benchmark than either technique employed in isolation.

AUTOBAXBUILDER represents an advancement in security benchmark creation by automating the generation of complex security tests. This automation demonstrably reduces the manual effort traditionally required for benchmark development. Quantitative analysis indicates an average scenario generation time of 2 hours using AUTOBAXBUILDER, a 33% reduction compared to the 3-hour average time required when benchmarks are constructed by human experts. This increased efficiency allows for more frequent and comprehensive security assessments.

Introducing AUTOBAXBENCH: A Rigorous Evaluation Framework

AUTOBAXBENCH is a newly developed benchmark designed for comprehensive security evaluation, consisting of 40 distinct security tasks. These tasks are not uniform in complexity; they are deliberately constructed to represent a spectrum of difficulty levels, ranging from relatively simple vulnerabilities to more challenging, multi-stage exploits. This varying difficulty is intended to provide a granular assessment of security tools and techniques, allowing for differentiation in performance across a range of threat scenarios and facilitating a more nuanced understanding of system robustness. The benchmark’s design prioritizes practical, real-world applicability in assessing security posture.

BAXBENCH is a security evaluation framework designed to identify critical vulnerabilities by employing both end-to-end exploit development and functional correctness testing. End-to-end exploits simulate real-world attack scenarios, verifying if a vulnerability can be leveraged to compromise a system. Functional correctness testing, conversely, validates that the system operates as intended, identifying deviations from expected behavior that may indicate underlying vulnerabilities. This dual approach aims to provide a more robust and comprehensive security assessment than relying on either method in isolation, ensuring that both exploitable weaknesses and logical errors are detected.

AUTOBAXBENCH employs a dual methodology for security assessment, utilizing both End-to-End Exploits and Functional Correctness Testing. End-to-End Exploits simulate real-world attack scenarios, verifying if a system can be compromised through a complete exploit chain. Simultaneously, Functional Correctness Testing validates that the system operates as designed, identifying deviations from expected behavior that could indicate vulnerabilities. This combined approach provides a holistic evaluation, detecting issues that either exploit-focused or functionality-focused testing might miss in isolation, and ensuring a more comprehensive security profile.

AUTOBAXBENCH represents a significant expansion of the BAXBENCH framework, more than doubling its size in terms of included security tasks. This increase in scale is further reflected in the complexity of individual evaluation scenarios; the average scenario within the AUTOBAXBENCH MEDIUM difficulty level incorporates 3 endpoints, compared to an average of 1.9 endpoints per scenario in the original BAXBENCH. This higher endpoint count per scenario facilitates a more comprehensive security assessment by requiring evaluation tools to navigate and interact with more complex system architectures and potential attack surfaces.

Comprehensive Analysis: Augmenting Automation with Established Methods

While automated benchmarking, such as that provided by AUTOBAXBENCH, offers efficient vulnerability detection, a truly robust security posture necessitates integration with established techniques. Static analysis, which examines code without execution, identifies potential weaknesses based on coding standards and known vulnerabilities, while dynamic analysis observes the software during runtime to uncover issues like memory leaks or unexpected behavior. These traditional methods don’t replace automation; instead, they offer complementary insights and a deeper understanding of the system’s security profile. By combining the speed of automated testing with the nuanced perspective of manual review, developers can build a multi-layered defense, addressing a wider range of potential exploits and ensuring a more resilient application.

The AUTOBAXBENCH framework proves highly effective in identifying critical vulnerabilities like resource exhaustion, a common attack vector where malicious actors attempt to overwhelm a system by consuming its available resources. This detection capability extends beyond a single threat, indicating the framework’s broader applicability to diverse attack scenarios. By successfully pinpointing resource exhaustion flaws, AUTOBAXBENCH demonstrates its potential to safeguard against denial-of-service attacks and other exploits that rely on disrupting system functionality through resource depletion, thereby bolstering overall software resilience and security posture.

AUTObAxBENCH actively assists developers in strengthening software security by pinpointing vulnerabilities aligned with the Common Weakness Enumeration (CWE). This benchmark doesn’t simply flag issues; it categorizes them according to a widely recognized standard, enabling developers to address the most critical weaknesses first. By focusing on prevalent flaws – such as improper input validation or buffer overflows – development teams can efficiently allocate resources and prioritize remediation efforts. This targeted approach moves beyond generic security patching, fostering the creation of software demonstrably more resistant to a broad spectrum of attacks and ultimately improving overall application resilience.

The creation of robust security benchmarks is often hampered by substantial financial constraints, but the AUTOBAXBENCH framework demonstrably lowers the barrier to entry. Researchers successfully generated a comprehensive benchmark suite of 40 distinct security scenarios at a total cost of under $160, translating to an exceptionally low average of $3.9 per scenario. This cost-effectiveness stems from an innovative reliance on automation and readily available cloud resources, effectively democratizing access to thorough security testing previously limited by budget restrictions and enabling wider adoption of proactive vulnerability assessment within software development lifecycles.

Towards Proactive and Adaptive Security: The Future of Vulnerability Mitigation

The convergence of reinforcement learning and large language model-based code generation presents a pathway toward truly proactive cybersecurity. This approach moves beyond traditional reactive security measures by enabling the creation of code capable of autonomously identifying and mitigating vulnerabilities. Rather than relying on pre-defined rules or human intervention, a system trained through reinforcement learning can dynamically adapt to novel threats as they emerge. The LLM component facilitates the generation of potential code fixes, while the reinforcement learning algorithm evaluates their effectiveness in a simulated environment, iteratively refining the code’s ability to ‘self-heal’. This continuous learning loop allows the system to not only address known vulnerabilities but also to anticipate and defend against future attacks, effectively building resilience into the software itself and reducing the burden on security professionals.

The increasing prevalence of Representational State Transfer (REST) APIs in modern software demands a robust evaluation framework capable of assessing security vulnerabilities within these architectures. Consequently, efforts are underway to broaden the scope of the AUTOBAXBENCH benchmark, integrating a more diverse collection of REST APIs and their corresponding OpenAPI Specifications. This expansion is critical, as current evaluations often focus on limited API types, failing to capture the complex security challenges presented by real-world applications. By encompassing a wider range of API designs and functionalities, AUTOBAXBENCH aims to provide a more comprehensive and realistic assessment of automated code repair tools, ultimately fostering the development of systems better equipped to defend against evolving threats in contemporary software landscapes.

The sustained utility of any automated security benchmark hinges on its ability to mirror the ever-shifting landscape of actual cyber threats. Consequently, continuous refinement of the evaluation framework is paramount; static benchmarks quickly become obsolete as attackers devise novel techniques. This iterative process demands the incorporation of real-world attack data – gleaned from honeypots, incident reports, and threat intelligence feeds – to identify emerging vulnerabilities and evasion tactics. By regularly updating the benchmark with these insights, researchers can ensure its ongoing effectiveness in assessing the robustness of code generation and repair tools, and accurately gauge their capacity to defend against contemporary threats. This adaptive approach moves beyond theoretical vulnerability assessments to reflect the practical realities of modern cybersecurity, fostering a more resilient software ecosystem.

The demanding nature of contemporary security challenges is underscored by recent performance metrics from the AUTOBAXBENCH benchmark; even CLAUDE-4.5 SONNET, a state-of-the-art large language model, achieves a security pass rate of only 25% on the benchmark’s ‘HARD’ scenarios. This low success rate isn’t a reflection of the model’s overall capabilities, but rather a deliberate design feature of AUTOBAXBENCH, which focuses on complex, nuanced security vulnerabilities. The benchmark’s rigor demonstrates that current automated code generation and repair tools still face significant hurdles in reliably addressing sophisticated threats, emphasizing the need for continued research and development in proactive security measures and robust evaluation methodologies.

The pursuit of robust LLM evaluation, as detailed in the introduction of AUTOBAXBUILDER, echoes a fundamental principle of efficient design. The system actively minimizes extraneous effort in benchmark creation, aligning with the belief that complexity often obscures true understanding. As Bertrand Russell observed, “The point of contact between two disciplines is always a source of illumination.” AUTOBAXBUILDER illuminates the intersection of LLM technology and code security, providing a focused, automated approach to vulnerability assessment. By streamlining the benchmark generation process, it achieves a form of ‘lossless compression’ of effort, concentrating resources on identifying critical weaknesses rather than laborious manual construction.

What Lies Ahead?

AUTOBAXBUILDER automates a necessary evil: benchmark creation. But automation merely shifts the problem, it doesn’t solve it. The core challenge remains: defining ‘secure code’. Current metrics focus on vulnerability detection. This is reactive. Future work must emphasize proactive security-generating code demonstrably resistant to exploitation, not simply identifying flaws after they appear.

Synthetic benchmarks are useful, yet abstractions age, principles don’t. The pipeline’s reliance on existing vulnerability patterns is a limitation. True progress requires modeling attacker creativity-going beyond known exploits to anticipate novel attack vectors. Every complexity needs an alibi. The system’s internal complexity must be justified by a commensurate increase in the sophistication of generated tests.

Ultimately, the field needs less emphasis on ‘beating the benchmark’ and more on building fundamentally secure systems. LLMs can generate code, but they cannot guarantee its security. The focus should shift from evaluation to verification-developing methods to formally prove the absence of vulnerabilities, not just their presence.

Original article: https://arxiv.org/pdf/2512.21132.pdf

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