HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation

arXiv:2601.13864 · cs.CR, cs.AI · Submitted 2026-01-20 · Read on arXiv

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Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: Today's paper: "HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation".

Elias: Large language models (LLMs) are increasingly used for hardware and firmware code generation, but existing studies primarily evaluate functional correctness while largely overlooking security.

Nadia: First, who's behind it and why it matters.

Title and authors: Nadia: So, we’ve discussed what HardSecBench is and how it’s constructed, focusing on separating functional and security requirements within a multi-agent pipeline. Now let's look at the actual summary of the paper to see what they claim this benchmark achieves.

Elias: The summary explains that the primary motivation is addressing the gap in research where LLMs are evaluated primarily on functional correctness while security is largely ignored.

Priya: It’s interesting how they frame it—they aren't just saying LLMs are bad at security; they are designing a specific testbed to systematically assess their awareness under realistic specifications.

Nadia: They introduce HardSecBench as a benchmark with nine hundred twenty-four tasks spanning Verilog RTL and firmware-level C, covering seventy-six hardware-relevant Common Weakness Enumeration entries.

Elias: The core of the summary is that each task includes a structured specification, a secure reference implementation satisfying both functional and security requirements, and executable tests for verification.

Priya: They emphasize that the goal isn't just to check if code runs, but whether it actually implements protections checked by security requirements.

Nadia: And they detail the four stages of their pipeline: Seed Generator, Architect Agent creating the specification Pi separating R f i and R s i, the Expert Agent synthesizing a golden implementation in separate branches, and finally the Tester Agent deriving atomic test harnesses.

Elias: The summary highlights that this entire process is designed to scale benchmark construction and enable security evaluation even under specifications that don't reveal security intent.

Priya: It sounds like they’ve built a very sophisticated testing mechanism specifically tailored to probe the security awareness of these code generation models in a hardware context.

Nadia: That’s the essence of it; they are moving beyond simple functional checks to evaluate how well an LLM understands and implements security constraints embedded in a structured specification.

Elias: And they lay out the evaluation methodology clearly, distinguishing between single-attempt and iterative refinement settings to separate intuition from collaborative fixing ability.

Priya: It’s important that they are using simulation evidence from those targeted harnesses for scoring, which aims to avoid subjective judging entirely by focusing on observable security behaviors.

Nadia: So, in short, the paper summarizes the introduction of HardSecBench as a systematic way to evaluate LLMs for hardware code generation by forcing them to implement specific security requirements defined in structured specifications.

The paper's summary: Elias: Moving on from what we know about the setup, let’s talk about what the authors suggest are the improvements or design choices they made to make this work effective.

Nadia: They suggest several key methodological improvements, starting with designing a multi-agent construction pipeline that decouples synthesis from verification and grounds evaluation in execution evidence.

Priya: That decoupling is vital; it directly addresses the issue where test harnesses might accidentally encode implementation details, which they want to prevent through strict isolation.

Elias: They also propose an Arbiter Agent driven iterative refinement process, where this agent analyzes runtime evidence from the requirement-level harnesses to pinpoint exactly where a mismatch occurs.

Nadia: That feedback loop is designed to guide repair by identifying whether the failure is in the specification, the implementation, or even the harness itself.

Priya: I think that targeted feedback mechanism sounds much more robust than just letting models guess how to fix things based on general functional errors alone.

Elias: They also propose adopting a Pass@k metric for security requirements alongside standard functional pass rates to give a unified way of quantifying generation stability under different prompting conditions.

Nadia: That Pass@k metric, combined with prompt sensitivity analysis, allows them to measure how explicit security guidance, like Hint two actually impacts the security pass rates relative to general coding ability.

Priya: It seems like they are pushing for a more granular analysis of the performance metrics rather than just looking at one overall score across all models.

Elias: Furthermore, they suggest domain-specific fine-tuning for hardware-specialized models, quantifying exactly how much security performance gains are achieved across different CWE categories after this specialization.

Nadia: That is a very practical suggestion; it suggests that specialized training can give us measurable gains in mitigating specific types of hardware vulnerabilities, like power or clock issues.

Priya: I wonder if they acknowledge any limitations here; for instance, what they say the method itself doesn't do is important for understanding its real-world applicability.

Elias: Yes, and they do flag that performance tends to be lowest on categories related to "power, clock, thermal, and reset," as well as "memory and storage," which points to difficulties with temporal behavior or incomplete handling of physical access considerations.

Nadia: So the improvement isn't just about building a better benchmark; it’s about developing a framework that allows us to precisely diagnose *why* an LLM fails on specific hardware security challenges.

The paper's improvements: Priya: So, wrapping up what we’ve heard, the main implication seems to be that we need these systematic benchmarks like HardSecBench to move beyond simple functional testing when evaluating AI for hardware code generation.

Nadia: Exactly; the paper demonstrates that strong functional pass rates don't guarantee security compliance, and models often fail to implement necessary protections when only given functional requirements.

Elias: The work suggests that security awareness isn't just a side effect of general coding strength; it needs explicit guidance or specialized training to surface those specific security behaviors.

Priya: I think the most significant implication is the creation of a rigorous testing environment that forces models to confront security requirements in a way that’s observable and quantifiable.

Nadia: Indeed, by using the structured pipeline and evaluation settings described in "HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation," they provide a testbed for assessing security awareness under realistic specifications.

Elias: It highlights that we need to be careful about relying on AI for critical hardware design tasks without this kind of explicit security verification framework in place.

Priya: It’s a solid piece of work because it doesn't just point out a problem; it builds the tools to measure how much effort is actually required from an AI to achieve secure code generation.

Nadia: That’s right, so if you want to understand the security risks associated with LLM-generated hardware designs, checking out HardSecBench is definitely something you should look into.

Elias: It gives us a clearer picture of the current state of play and where we need to focus our efforts next as researchers in this area.

Priya: Well, that’s all for this discussion on "HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation."

Conclusion: Nadia: So we've covered the mechanics of HardSecBench, which is this benchmark designed to rigorously test how much security awareness LLMs actually have when they’re tasked with generating hardware code for things like Verilog and firmware C.

Elias: It really shows that functional correctness isn't enough; there are specific security requirements that need to be implemented, and this paper lays out a way to measure if the AI is paying attention to those requirements during the process.

Priya: From what I've seen of the data, it seems like the main finding is that models often pass functional tests easily but fail when they have to actively implement protections against common weaknesses enumerated in CWEs.

Nadia: That’s right; we saw that strong functional pass rates are substantially higher than security pass rates, which is a pretty stark reality for these systems.

Elias: And the correlation between general code generation capability and security potential isn't as tight as some might expect, suggesting that just being generally good at coding doesn't automatically mean the AI understands what needs protecting.

Priya: I agree; the analysis on prompt sensitivity showing that Hint two delivers larger improvements implies that security expertise is latent in these models but requires explicit guidance to activate it.

Nadia: And looking at the granular performance analysis, it’s clear where the weaknesses are; they struggled most with categories like power, clock, thermal issues, and memory handling.

Elias: That makes sense from a cryptographic standpoint; temporal behavior and physical access considerations are often where the assumptions in a design break down under real-world conditions.

Priya: It really tells us that we need to focus our privacy and measurement research on those specific domains because that’s where the current AI limitations are most apparent when dealing with hardware security.

Nadia: So, as we wrap up this discussion on HardSecBench, it’s clear this work provides a structured way for researchers to probe the security awareness of these code generation models in a very concrete setting.

Elias: It gives us a much clearer yardstick for evaluating whether an AI is just guessing or if it's actually following defined security constraints.

Priya: I think the real impact here is pushing us toward creating better fine-tuning methodologies that specifically target those weaker areas we identified, like memory and storage issues.

Nadia: Exactly; understanding these failure modes allows us to guide future training recipes much more effectively for hardware-focused LLMs.

Elias: This study on HardSecBench is a valuable tool for anyone trying to build robust hardware tools using AI assistance, because it shows the gap between what we ask for and what the AI actually delivers.

Priya: It sets a high bar for how we should be evaluating these models moving forward, focusing on measurable security outcomes rather than just general code quality.

University of Science and Technology of China

cs.CR, cs.AI

Submitted: 2026-01-20

Updated: 2026-06-21

Journal ref: Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-26), 2026, pp. 500-508

DOI: 10.24963/ijcai.2026/57

Code: https://github.com/chenqirui2002/HardSecBench

Project page: https://moonshotai.github.io/Kimi-K2

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Importance score: 91/100

The gist: Large language models (LLMs) are increasingly used for hardware and firmware code generation, but existing studies primarily evaluate functional correctness while largely overlooking security.

Key concepts

HardSecBench: A benchmark designed to evaluate the security awareness of LLMs. It contains 924 tasks across Verilog and firmware-C, covering 76 hardware-relevant Common Weakness Enumeration (CWE) entries. The goal is to test if LLMs generate code that implements required security protections.
Multi-agent construction pipeline: A four-stage process used to build the benchmark tasks. It involves a Seed Generator, an Architect Agent, an Expert Agent that synthesizes the secure implementation, and a Tester Agent that creates deterministic test harnesses. This decouples synthesis from verification.
Single-Attempt Evaluation: An evaluation setting where models are tested only on functional requirements without security feedback. It measures a model's innate security intuition by checking if it adds protections beyond the basic functionality when no explicit guidance is given.
Iterative Refinement Evaluation: A workflow simulating collaboration. The model generates code, functional tests run, and feedback is used to fix functional issues iteratively. Security performance is then assessed on the final implementation to separate security awareness from functional coding ability.

Terminology

Benchmark — 43× in this paper · explained in 2 episodes
Nadia — 18× in this paper · explained in 2 episodes
Verification — 18× in this paper · explained in 2 episodes
Elias — 17× in this paper · explained in 2 episodes
Iterative Refinement — 9× in this paper · explained in 3 episodes
Common Weakness Enumeration — 8× in this paper
Fine-Tuning — 7× in this paper · explained in 5 episodes
Benchmarking — 6× in this paper · explained in 3 episodes
Arbiter — 4× in this paper · explained in 1 episode
Consistency — 4× in this paper · explained in 3 episodes
Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
Verilog Register Transfer Level — 4× in this paper
Trace — 3× in this paper · explained in 2 episodes
Aggregation — 2× in this paper · explained in 1 episode
Core — 2× in this paper · explained in 2 episodes
Formal Verification — 2× in this paper · explained in 2 episodes
Hallucination — 2× in this paper · explained in 3 episodes
International Conference on LLM-Aided Design — 2× in this paper
Knowledge Graph — 2× in this paper · explained in 7 episodes
Robustness — 2× in this paper · explained in 5 episodes
Agentic Coding — 1× in this paper · explained in 1 episode
Cooperation — 1× in this paper · explained in 2 episodes
Decoupling — 1× in this paper · explained in 2 episodes
Failure Modes — 1× in this paper · explained in 3 episodes
LLM Aided Design Workshop — 1× in this paper
Training-Free Framework — 1× in this paper · explained in 2 episodes
Verifier — 1× in this paper · explained in 2 episodes

Summary

Large language models (LLMs) are increasingly used for hardware and firmware code generation, but existing studies primarily evaluate functional correctness while largely overlooking security. This work introduces HardSecBench, a benchmark with 924 tasks spanning Verilog Register Transfer Level (RTL) and firmware-level C, covering 76 hardware-relevant Common Weakness Enumeration (CWE) entries to systematically assess the security awareness of LLMs for hardware code generation.

HardSecBench Overview

HardSecBench is a benchmark designed to evaluate the security awareness of LLMs in generating hardware designs by incorporating a multi-agent construction pipeline. The benchmark consists of 924 tasks covering Verilog and firmware-level C, and it spans 76 Common Weakness Enumeration (CWE) entries. Each task includes a structured specification, a secure reference implementation, and executable tests. The goal is to move beyond functional correctness by evaluating whether LLM-generated code implements protections checked by security requirements.

Benchmark Construction Pipeline

The construction of HardSecBench utilizes a multi-agent pipeline that decouples synthesis from verification and grounds evaluation in execution evidence. This process involves four stages coupled by a single structured specification:

Seed Generator: Converts each CWE definition into a seed specifying the implementation language and a 1–2 sentence scenario where the weakness can arise.
Architect Agent: Expands each seed into a structured specification Pi, separating functional requirements R f i from security requirements R s i.
Expert Agent: Synthesizes the golden implementation satisfying both functional and security requirements in separate branches to avoid logic coupling between implementation and verification.
Tester Agent: Derives atomic test harnesses with a one-to-one mapping to requirements in R f i and R s i, emitting standardized PASS/FAIL traces for deterministic parsing.

Evaluation Methodology

The evaluation methodology is designed to avoid subjective judging by scoring security using simulation evidence from targeted harnesses that actively trigger security relevant behaviors. Two primary evaluation settings are employed:

Single-Attempt Evaluation: This measures the model’s security intuition by checking whether it adds protections beyond functional requirements when given only functional requirements and no execution feedback. It requires a compilable implementation, allowing up to three rounds of compilation and fixing using only compiler error messages before running the full functional and security harnesses once.
Iterative Refinement Evaluation: This simulates a collaborative workflow where the model produces a compilable implementation, functional harnesses are run, and a Collaborator provides feedback on functional failures. This loop repeats until all functional requirements pass or an iteration limit is reached, and then security performance is evaluated on the final implementation to separate security awareness from functional defects.

Key Findings

The experiments reveal that strong functional pass rates are substantially higher than security pass rates, indicating that models often fail to implement protections when given only functional requirements. Furthermore, security scores have weak correlation with general code generation capability, which suggests that security compliance is not fully explained by overall coding strength. The analysis of prompt sensitivity shows that Hint 2 delivers larger improvements for closed-source models, implying security expertise is present but dormant without explicit guidance. Finally, the granular analysis shows that performance is lowest on categories related to power, clock, thermal, and reset, as well as memory and storage, reflecting difficulties with temporal behavior and incomplete handling of physical access considerations.

Contributions

The primary contributions of this work are:

** We formulate a systematic evaluation setting for security awareness in LLM-generated hardware designs, offering a rigorous testbed for secure hardware code generation;**

"We develop a multi-agent construction pipeline that synthesizes benchmark samples, producing testable specifications and corresponding simulatable harnesses;

"We propose HardSecBench, a benchmark of 924 Verilog and firmware-C tasks spanning 76 CWE entries;

and the comprehensive evaluation of a wide range of SOTA models regarding the security risks of LLM-generated hardware designs.

The gist

HardSecBench is a benchmark with 924 tasks spanning Verilog Register Transfer Level (RTL) and firmware-level C, covering 76 hardware-relevant Common Weakness Enumeration (CWE) entries to systematically assess the security awareness of LLMs for hardware code generation.

Table 1: Main evaluation of LLMs under security settings.

(The table provides quantitative results summarized across various models and hint levels.)

Figure 5: Security pass rates across different security hint levels for representative models.

(This figure illustrates the performance differences based on the explicit guidance level provided to the models.)

Figure 6: Correlation between code generation capability and security potential across prompting levels.

(This figure demonstrates how security performance increases with general coding capability under explicit guidance.

Improvements for AI systems

Here are specific improvements to existing AI systems based on the HardSecBench research:

Enhanced Security-Aware Code Generation Agents:

Identify and integrate security reasoning capabilities into LLM agents designed for hardware (RTL/Firmware) code generation. The improved system will be able to generate code that not only meets functional specifications but is explicitly verified against a structured set of security requirements (CWEs) during the synthesis phase.

Multi-Agent Benchmark Construction Pipeline:

Develop an automated, multi-agent pipeline for generating high-quality, diverse benchmark tasks. This system will use agents to synthesize structured specifications that rigorously separate functional requirements from explicit security constraints, ensuring the resulting benchmark is systematically designed rather than manually curated.

Decoupled Artifact Synthesis with Information Isolation:

Implement a synthesis process where the agent generating the secure implementation and the agent generating the requirement-level harnesses operate in strictly isolated contexts, sharing only a structured specification. This prevents logic coupling where test harnesses inadvertently encode implementation details, leading to more reliable security evaluation metrics.

Arbiter-Driven Iterative Refinement for Automated Bug Repair:

Introduce an automated feedback loop driven by an Arbiter Agent. This agent will analyze runtime evidence from requirement-level harnesses to pinpoint the source of a mismatch (specification, implementation, or harness) and issue targeted feedback for repair. This will allow models to iteratively fix functional defects in a way that is guided by verification evidence, rather than relying solely on subjective human collaboration.

Robust Security Evaluation Metrics (Pass@k):

Adopt the Pass@k metric for security requirements alongside standard functional pass rates. This allows for a unified, rigorous quantification of generation stability under different prompting conditions, enabling precise measurement of security awareness across diverse model families and hint levels (Hint 0, 1, 2).

Prompt Sensitivity Analysis Integration:

Incorporate prompt sensitivity analysis into the model evaluation framework. The system will be able to quantify how explicit security guidance (Hint 2) affects security pass rates relative to general code generation capability. This provides actionable insights for developers on the necessary level of explicitness required from LLMs to surface critical security behaviors.

Domain-Specific Fine-Tuning for Security Baseline Improvement:

Develop a methodology where hardware-specialized fine-tuning (e.g., on CodeV-R1 or RTLCoder) is systematically applied to base models. The system will be able to quantify the exact security performance gains achieved by this specialization across different CWE categories, identifying which domain knowledge areas (e.g., physical access vs. memory issues) benefit most from specialized training under varying levels of security hints.

Granular Vulnerability Pattern Recognition:

Improve the ability of the system to diagnose failures at the sub-CWE level (e.g., distinguishing between Memory and Storage Issues and Privilege Separation). This granular analysis will allow researchers to pinpoint exactly which types of hardware vulnerabilities LLMs struggle with, guiding future training recipes toward mitigating these specific systemic weaknesses.