AttackonCTF: Defending Hardware Security Competition Benchmarks in the Age of LLMs
- URL: http://arxiv.org/abs/2606.15809v1
- Date: Sun, 14 Jun 2026 13:22:27 GMT
- Title: AttackonCTF: Defending Hardware Security Competition Benchmarks in the Age of LLMs
- Abstract summary: Hardware security competitions such as HackTheSilicon serve as benchmarking platforms for evaluating vulnerability detection methods.<n>We propose the first LLM-oriented, semantics-preserving obfuscation framework for these benchmarks.<n>On HackTheSilicon, the framework reduces LLM-based detection accuracy by 50% with only 10% obfuscation and by 78.6% under complete obfuscation.
- Score: 16.57778619842239
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Hardware security competitions such as HackTheSilicon serve as benchmarking platforms for evaluating vulnerability detection methods and for training humans and AI. However, our study reveals that LLMs threaten their validity. Instead of genuine security reasoning, detectors exploit a diff-style syntactic comparison, achieving an 83% detection rate, undermining fair evaluation. To mitigate this, we propose the first LLM-oriented, semantics-preserving obfuscation framework for these benchmarks. Unlike IP-protection approaches, it applies human-readable transformations and controlled diff-noise while preserving functionality. On HackTheSilicon, the framework reduces LLM-based detection accuracy by 50% with only 10% obfuscation and by 78.6% under complete obfuscation, restoring benchmark reliability.
Related papers
- CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery [51.07538881798502]
Large language models (LLMs) offer a promising source of domain knowledge to complement statistical inference.<n>We propose CauTion, a framework that reliably integrates LLM domain knowledge into an ensemble of statistical causal discovery algorithms.<n>CauTion consistently outperforms both data-centric and LLM-augmented baselines.
arXiv Detail & Related papers (2026-06-02T13:07:43Z) - Turning Bias into Bugs: Bandit-Guided Style Manipulation Attacks on LLM Judges [65.92623263645139]
We introduce BITE, a black-box adversarial framework that learns semantics-preserving edits to mislead an LLM judge.<n>BITE achieves an attack success rate exceeding 65% and raises scores by 1-2 points on a 9-point scale.<n>Our findings expose a fundamental weakness in the LLM-as-a-judge paradigm and motivate robust, attack-aware evaluation.
arXiv Detail & Related papers (2026-05-24T05:24:09Z) - Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection [5.902327401962825]
We present Reflect-Guard, a method that augments LLM-based safety classifiers with chain-of-thought self-reflection capabilities.<n>Our approach distills analytical reasoning from GPT-4o-mini into structured annotations, then trains Llama-Guard-3-8B via QLoRA to generate logical self-reflections.<n>Using only 1000 training examples and updating just 0.5% of model parameters, Reflect-Guard achieves substantial improvements on two challenging benchmarks.
arXiv Detail & Related papers (2026-05-24T02:58:21Z) - SecPI: Secure Code Generation with Reasoning Models via Security Reasoning Internalization [50.71047638695205]
Reasoning language models (RLMs) are increasingly used in programming.<n>Yet, even state-of-the-art RLMs frequently introduce critical security vulnerabilities in generated code.<n>We present SecPI, a fine-tuning pipeline that teaches RLMs to internalize structured security reasoning.
arXiv Detail & Related papers (2026-04-04T04:29:11Z) - Why LLMs Fail: A Failure Analysis and Partial Success Measurement for Automated Security Patch Generation [0.0]
This study analyzes 319 Large Language Models (LLMs)generated security patches across 64 Java vulnerabilities from the Vul4J benchmark.<n>Using tri-axis evaluation, the analysis reveals that only 24.8% of patches achieve full correctness, while 51.4% fail both security and functionality.<n>The proposed Security Repair Score (SRS) quantifies this gap, showing LLMs preserve functionality (mean 0.832) but struggle with security (mean 0.251)
arXiv Detail & Related papers (2026-03-10T05:34:56Z) - RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
arXiv Detail & Related papers (2026-01-30T08:29:01Z) - VulSolver: Vulnerability Detection via LLM-Driven Constraint Solving [23.259666449166456]
Traditional vulnerability detection methods rely heavily on predefined rule matching.<n>We propose a constraint-solving approach powered by large language models (LLMs)<n>We assess VULSOLVER on the Benchmark, achieving 97.85% accuracy, 97.97% F1-score, and 100% recall.
arXiv Detail & Related papers (2025-08-31T14:49:48Z) - VADER: A Human-Evaluated Benchmark for Vulnerability Assessment, Detection, Explanation, and Remediation [0.8087612190556891]
VADER comprises 174 real-world software vulnerabilities, each carefully curated from GitHub and annotated by security experts.<n>For each vulnerability case, models are tasked with identifying the flaw, classifying it using Common Weaknession (CWE), explaining its underlying cause, proposing a patch, and formulating a test plan.<n>Using a one-shot prompting strategy, we benchmark six state-of-the-art LLMs (Claude 3.7 Sonnet, Gemini 2.5 Pro, GPT-4.1, GPT-4.5, Grok 3 Beta, and o3) on VADER.<n>Our results show that current state-of-the-
arXiv Detail & Related papers (2025-05-26T01:20:44Z) - AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security [74.22452069013289]
AegisLLM is a cooperative multi-agent defense against adversarial attacks and information leakage.<n>We show that scaling agentic reasoning system at test-time substantially enhances robustness without compromising model utility.<n> Comprehensive evaluations across key threat scenarios, including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM.
arXiv Detail & Related papers (2025-04-29T17:36:05Z) - Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection [9.269926508651091]
Large language models (LLMs) have shown limited ability on safety-critical code tasks such as vulnerability detection.<n>We propose prompting strategies that integrate natural language instructions of vulnerabilities with contrastive chain-of-thought reasoning.<n>Our findings demonstrate that security-aware prompting techniques can be effective alternatives to the laborious, hand-crafted rules of static analyzers.
arXiv Detail & Related papers (2024-12-16T18:08:14Z) - LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Most LLMs struggle on SummEdits, with performance close to random chance.
The best-performing model, GPT-4, is still 8% below estimated human performance.
arXiv Detail & Related papers (2023-05-23T21:50:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.