LLMSec-AV: A Vulnerability Taxonomy and LLM-Driven Software Weakness Discovery Framework for Autonomous Vehicles

• URL: http://arxiv.org/abs/2609.09386v1
• Date: Tue, 08 Sep 2026 19:39:25 GMT
• Title: LLMSec-AV: A Vulnerability Taxonomy and LLM-Driven Software Weakness Discovery Framework for Autonomous Vehicles
• Abstract summary: Large language models (LLMs) with explicit automated-vehicle (AV) security knowledge improve weakness detection beyond rule-based tools.<n>We developed an AV vulnerability taxonomy with 18 weakness classes, vulnerability records, security advisories, and AV-security literature, and integrated it into LLM-based Security Analysis for Automated Vehicles (LLMSec-AV)<n>LLMSec-AV introduces an AV vulnerability taxonomy for weakness discovery and shows that LLMs can complement conventional analyzers by identifying and organizing safety-relevant findings in real AV software.
• Score: 6.341317643879287
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated vehicles rely on millions of lines of safety-critical software, yet general-purpose analyzers do not understand which code can affect vehicle motion. This study asks whether large language models (LLMs) with explicit automated-vehicle (AV) security knowledge improve weakness detection beyond rule-based tools. We developed an AV vulnerability taxonomy with 18 weakness classes from vulnerability records, security advisories, and AV-security literature, and integrated it into LLM-based Security Analysis for Automated Vehicles (LLMSec-AV). Evaluated on Autoware, the framework decomposed 770 translation units into 4,673 functions and analyzed 161 functions under four prompting conditions involving taxonomy context, retrieval from 374 prior disclosures, and multi-step analysis. Findings were compared with 46 weakness locations mined from upstream fixes and a flag-volume-matched permutation baseline. CodeQL, Semgrep, cppcheck, and the Clang Static Analyzer evaluated the same code, with AV-specific rules added to CodeQL and Semgrep. Generated fuzzing harnesses were tested using AFL++ and sanitizers. LLM conditions recovered up to 76% of the 46 known weakness locations, outperforming conventional analyzers. CodeQL, Semgrep, and the Clang Static Analyzer matched none, while cppcheck matched one despite 1,301 alerts. Unaided prompting achieved similar detection performance, showing that the taxonomy did not drive recall. However, taxonomy context increased the share of findings assigned to a weakness class from near zero to over 80%, improving interpretability and triage. Six of the 18 classes could not be directly represented as static-analysis rules. LLMSec-AV introduces an AV-specific, machine-readable vulnerability taxonomy for weakness discovery and shows that LLMs can complement conventional analyzers by identifying and organizing safety-relevant findings in real AV software.

Related papers

• Vulnerability Localization Benchmark: Measuring Agentic Security Analysis at Repository Scale [54.83971397981572]
  Language-model agents increasingly operate over complete software repositories.<n> cybersecurity evaluations measure whether they can detect, reproduce, or repair vulnerabilities rather than whether they can locate the relevant code.<n>We study vulnerability localization: given a weakness class and an unfamiliar repository, identify the implementation files associated with that weakness.
  arXiv  Detail & Related papers  (2026-09-14T17:44:51Z)
• Beyond Static Guarantees: Measuring the Static-Pass Dynamic-Fail Gap in Security-Sensitive and LLM-Generated Python Code [2.1410799064827235]
  This paper introduces the StaticPass Dynamic-stage agentic pipeline combining static scanning, Common Weaknession reasoning, and autonomous exploit verification in Docker containers.<n>We evaluate 1,355 samples from SecurityEval, RedCode, and CyberNative datasets.<n> Dynamic verification confirmed or partially confirmed exploitability in 95 files, yielding an inclusive pipeline rate of 14.53%.
  arXiv  Detail & Related papers  (2026-09-09T19:02:55Z)
• LLM-Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles [6.008950554571275]
  We investigate whether large language models (LLMs) can automate the static analysis of an autonomous-driving stack.<n>We perform compiler-precise static analysis across 185 packages, identifying 1,375 decision rules, 2,274 validation checks, and 482 input-to-safety-output flows.<n>Two local open-weight LLMs, a no-static-context ablation, and a naive-template baseline generate 3,700 artifact sets.<n>The main result is a build-integration failure taxonomy showing that 80% of first-shot compilation failures arise from dependency wiring rather than program logic.
  arXiv  Detail & Related papers  (2026-08-13T16:33:44Z)
• From Attack Simulation to SIEM Rule: Deterministic Detection-as-Code Synthesis with Probe-Level Traceability [51.56484100374058]
  Security teams simulate attacks against their own systems to check whether their monitoring would catch a real intruder.<n>A human bridges that gap by hand, reading each finding and writing the corresponding Sigma rule.<n>We show this translation can be partially automated when probes are drawn from a locked corpus.
  arXiv  Detail & Related papers  (2026-06-03T14:26:25Z)
• 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)
• 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)
• Multi-Agent Taint Specification Extraction for Vulnerability Detection [49.27772068704498]
  Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results.<n>We present SemTaint, a multi-agent system that strategically combines the semantic understanding of Large Language Models (LLMs) with traditional static program analysis.<n>We integrate SemTaint with CodeQL, a state-of-the-art SAST tool, and demonstrate its effectiveness by detecting 106 of 162 vulnerabilities previously undetectable by CodeQL.
  arXiv  Detail & Related papers  (2026-01-15T21:31:51Z)
• SeBERTis: A Framework for Producing Classifiers of Security-Related Issue Reports [8.545800179148442]
  SEBERTIS is a framework to train Deep Neural Networks (DNNs) as classifiers independent of lexical cues.<n>Our framework achieves a 0.9880 F1-score in detecting security-related issues of a curated corpus of 10,000 GitHub issue reports.
  arXiv  Detail & Related papers  (2025-12-17T01:23:11Z)
• ParaVul: A Parallel Large Language Model and Retrieval-Augmented Framework for Smart Contract Vulnerability Detection [43.41293570032631]
  ParaVul is a retrieval-augmented framework to improve the reliability and accuracy of smart contract vulnerability detection.<n>We develop Sparse Low-Rank Adaptation (SLoRA) for LLM fine-tuning.<n>We construct a vulnerability contract dataset and develop a hybrid Retrieval-Augmented Generation (RAG) system.
  arXiv  Detail & Related papers  (2025-10-20T03:23:41Z)
• 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)
• SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal [64.9938658716425]
  SORRY-Bench is a proposed benchmark for evaluating large language models' (LLMs) ability to recognize and reject unsafe user requests.<n>First, existing methods often use coarse-grained taxonomy of unsafe topics, and are over-representing some fine-grained topics.<n>Second, linguistic characteristics and formatting of prompts are often overlooked, like different languages, dialects, and more -- which are only implicitly considered in many evaluations.
  arXiv  Detail & Related papers  (2024-06-20T17:56:07Z)
• IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities [14.188864624736938]
  Large language models (or LLMs) have shown impressive code generation capabilities but they cannot do complex reasoning over code to detect such vulnerabilities.<n>We propose IRIS, a neuro-symbolic approach that systematically combines LLMs with static analysis to perform whole-repository reasoning for security vulnerability detection.
  arXiv  Detail & Related papers  (2024-05-27T14:53:35Z)

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.