Patch Validation in Automated Vulnerability Repair

• URL: http://arxiv.org/abs/2603.06858v1
• Date: Fri, 06 Mar 2026 20:22:36 GMT
• Title: Patch Validation in Automated Vulnerability Repair
• Abstract summary: We show that over 40% of patches validated as correct by basic tests fail under $textPoC+$ testing.<n>We suggest that tools should improve in three critical areas: root cause analysis, adherence to program specifications, and capturing developer intention.
• Score: 10.370450546423596
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated Vulnerability Repair (AVR) systems, especially those leveraging large language models (LLMs), have demonstrated promising results in patching vulnerabilities -- that is, if we trust their patch validation methodology. Ground-truth patches from human developers often come with new tests that not only ensure mitigation of the vulnerability but also encode extra semantics such as root cause location, optimal fix strategy, or subtle coding styles or conventions. And yet, none of the recent AVR systems verify that the auto-generated patches additionally pass these new tests (termed as $\text{PoC}^+$ tests). This is a subtle yet critical omission. To fill this gap, we constructed a benchmark, $\textrm{PVBench}$, with 209 cases spanning 20 projects. Each case includes basic tests (functional tests before the patch and the PoC exploit) as well as the associated $\text{PoC}^+$ tests. Evaluated on three state-of-the-art AVR systems, we find that over 40\% of patches validated as correct by basic tests fail under $\text{PoC}^+$ testing, revealing substantial overestimation on patch success rates. Analyzing these patches that are falsely labeled as correct, we suggest that AVR tools should improve in three critical areas: root cause analysis, adherence to program specifications, and capturing developer intention.

Related papers

• ExecCritic: Learn to Test, Test to Improve for Coding Agents [68.42713285082912]
  Execution feedback can guide coding agents toward correct repository repairs, but only when the tests capture the behavior requested by the issue.<n>We introduce ExecCritic, combining a test--verify--revise scaffold with a role-specific reinforcement learning recipe for training agents within it.<n>The scaffold separates test construction from source-code repair: a Test agent independently generates repository-native tests, a fail-closed harness qualifies and freezes them, and a Repair agent revises source code from their execution feedback without changing the tests.
  arXiv  Detail & Related papers  (2026-09-08T17:53:37Z)
• PatchBench: Evaluating AI Agents for Vulnerability Patching [9.083611812336539]
  We propose a new benchmark for evaluating AI agents on realistic vulnerability patching tasks.<n> PatchBench selects vulnerabilities whose ground-truth fixes lie outside the crash stack and uses vulnerability transplant and code mutations to migrate historical vulnerabilities into new repository contexts.<n>Across 11 state-of-the-art agents, including the top three AIxCC agents, the original PoC-only validations the patching task solve rate of agents by 1.83$times$ on average.
  arXiv  Detail & Related papers  (2026-09-03T16:44:22Z)
• Independent Patch Verification for Coding Agents with a Bidirectional Reconstruct-and-Verify Framework [49.16055123488827]
  coding agents powered by large language models can now generate code patches directly from bug reports.<n>No mechanism independently verifies whether it truly resolves the reported problem.<n>We propose RETRACE, a training-free post-generation verification framework.
  arXiv  Detail & Related papers  (2026-08-09T22:59:13Z)
• Beyond Fail-to-Pass: Iterative Hardening of Co-Generated Bug Reproduction Tests and Fixes [55.114939648705395]
  Large language models (LLMs) have made automated program repair increasingly practical for real-world bugs.<n>Bug reproduction tests (BRTs) help close this gap by turning a bug report into an executable, bug-specific signal.<n>We propose CoHarden, a co-generation framework that uses the Lax signal as an in-loop convergence criterion.
  arXiv  Detail & Related papers  (2026-07-22T07:30:07Z)
• ContraFix: Agentic Vulnerability Repair via Differential Runtime Evidence and Skill Reuse [10.503895811137095]
  Large language model (LLM) agents are increasingly used for automated vulnerability repair.<n>Recent empirical results show that these agents still struggle with real-world vulnerabilities.<n>We present ContraFix, an agentic framework that couples runtime evidence with reusable repair skills.
  arXiv  Detail & Related papers  (2026-05-17T13:48:25Z)
• Are Benchmark Tests Strong Enough? Mutation-Guided Diagnosis and Augmentation of Regression Suites [49.16055123488827]
  Insufficiently strong test suites can admit plausible yet semantically incorrect patches, inflating reported success rates.<n>We introduce STING, a framework for targeted test augmentation that uses semantically altered program variants as diagnostic stressors.<n>STING produces 1,014 validated tests spanning 211 instances and increases patch-region line and branch coverage by 10.8% and 9.5%.
  arXiv  Detail & Related papers  (2026-04-02T01:13:40Z)
• Reward Under Attack: Analyzing the Robustness and Hackability of Process Reward Models [68.45272703833209]
  We show that state-of-the-art PRMs are systematically exploitable under adversarial optimization pressure.<n>We introduce a three-tiered diagnostic framework that applies increasing adversarial pressure to quantify these vulnerabilities.<n>We release PRM-BiasBench and a diagnostic toolkit to enable robustness evaluation before deployment.
  arXiv  Detail & Related papers  (2026-02-20T23:38:03Z)
• Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers [90.50039419576807]
  Reinforcement Learning with Verifiable Rewards (RLVR) trains policies against automated verifiers to avoid costly human labeling.<n>To reduce vulnerability to verifier hacking, many RLVR systems collapse rewards to binary $0,1$ during training.<n>This choice carries a cost: it introduces textitfalse negatives (rejecting correct answers, FNs) and textitfalse positives (accepting incorrect ones, FPs)
  arXiv  Detail & Related papers  (2025-10-01T13:56:44Z)
• What Do They Fix? LLM-Aided Categorization of Security Patches for Critical Memory Bugs [46.325755802511026]
  We developLM, a dual-method pipeline that integrates two approaches based on a Large Language Model (LLM) and a fine-tuned small language model.<n>LM successfully identified 111 of 5,140 recent Linux kernel patches addressing OOB or UAF vulnerabilities, with 90 true positives confirmed by manual verification.
  arXiv  Detail & Related papers  (2025-09-26T18:06:36Z)
• Are "Solved Issues" in SWE-bench Really Solved Correctly? An Empirical Study [18.117047833029073]
  Most popular benchmarks for automated issue solving are SWE-bench and its human-filtered subset SWE-bench Verified.<n>This paper presents an in-depth empirical study of the correctness of plausible patches generated by three state-of-the-art issue-solving tools evaluated on SWE-bench Verified.
  arXiv  Detail & Related papers  (2025-03-19T14:02:21Z)
• SBEST: Spectrum-Based Fault Localization Without Fault-Triggering Tests [17.90798133817018]
  This study investigates the feasibility of using stack traces from crash reports as proxies for fault-triggering tests in Spectrum-Based Fault localization.<n>We propose SBEST, a novel approach that integrates stack trace information with test coverage data to perform fault localization when fault-triggering tests are missing.
  arXiv  Detail & Related papers  (2024-05-01T15:15:52Z)
• PS$^3$: Precise Patch Presence Test based on Semantic Symbolic Signature [13.9637348151437]
  Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score.
  arXiv  Detail & Related papers  (2023-12-06T10:04:15Z)
• RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair [75.40584530380589]
  We propose a novel Retrieval-Augmented Patch Generation framework (RAP-Gen) RAP-Gen explicitly leveraging relevant fix patterns retrieved from a list of previous bug-fix pairs. We evaluate RAP-Gen on three benchmarks in two programming languages, including the TFix benchmark in JavaScript, and Code Refinement and Defects4J benchmarks in Java.
  arXiv  Detail & Related papers  (2023-09-12T08:52:56Z)
• Test-based Patch Clustering for Automatically-Generated Patches Assessment [21.051652050359852]
  Overfitting happens when a patch is run and the test suite does not reveal any error, but the patch actually does not fix the underlying bug or it introduces a new defect that is not covered by the test suite. Our work aims to minimize the number of plausible patches that programmers have to review, thereby reducing the time required to find a correct patch. We introduce a novel light-weight test-based patch clustering approach called xTestCluster, which clusters patches based on their dynamic behavior.
  arXiv  Detail & Related papers  (2022-07-22T13:39:27Z)
• Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection [142.24869736769432]
  Adversarial patch attacks pose a serious threat to state-of-the-art object detectors. We propose Segment and Complete defense (SAC), a framework for defending object detectors against patch attacks. We show SAC can significantly reduce the targeted attack success rate of physical patch attacks.
  arXiv  Detail & Related papers  (2021-12-08T19:18:48Z)
• Exploring Plausible Patches Using Source Code Embeddings in JavaScript [1.3327130030147563]
  We trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it. These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program. We analyzed these similarity lists and found that plain document embeddings may lead to misclassification.
  arXiv  Detail & Related papers  (2021-03-31T06:57:10Z)

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.