AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair
- URL: http://arxiv.org/abs/2607.29422v1
- Date: Fri, 31 Jul 2026 13:42:51 GMT
- Title: AgenticRepair: Multi-Faceted Program Context Engineering for Agentic Vulnerability Repair
- Abstract summary: Vulnerability repair demands richer program context than general bug repair.<n>We present AgenticRepair, an agentic vulnerability repair framework that addresses the gaps through multi-faceted program context engineering.
- Score: 3.793192864512799
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Automated vulnerability repair aims to reduce the time and effort required to patch security flaws from a vulnerability triage report. Recent agentic AI approaches have shown promising results in automated program repair. However, vulnerability repair demands richer program context than general bug repair - context that security engineers routinely assemble in practice but that existing agentic approaches do not engineer. We identify three critical gaps: code-structure context capturing cross-file data flows and memory operation patterns, runtime-execution context revealing crash semantics and memory origins, and commit-history context recovering how fragile code patterns were introduced. We present AgenticRepair, an agentic vulnerability repair framework that addresses the gaps through multi-faceted program context engineering. AgenticRepair orchestrates three specialized LLM subagents to engineer the contexts, which are then embedded into the memory of a dedicated repair subagent for context-conditioned patch synthesis. Evaluated on SEC-Bench comprising 300 real-world instances with sanitizer-based patch verification, AgenticRepair achieves a 73% success rate, substantially outperforming the strongest baseline by 29%. Our ablation study confirms that the three context facets are mutually complementary, and that multi-agent scaffolding and base-model capacity each play an essential role. Collectively, these findings establish multi-faceted program context engineering as a promising design direction for agentic vulnerability repair.
Related papers
- RAVEN: Agentic RAG for Automated Vulnerability Repair [48.8954115363348]
We propose RAVEN, a scalable, efficient and autonomous framework for automated vulnerability repair.<n>RAVEN integrates an agentic retrieval-augmented generation (RAG) pipeline with controlled iterative repair in a unified framework.<n>RAVEN achieves an overall repair success rate of 83.13%, outperforming all existing state-of-the-art repair frameworks.
arXiv Detail & Related papers (2026-06-21T19:18:06Z) - RepoMirage: Probing Repository Context Reasoning in Code Agents with Perturbations [51.43574078961796]
We introduce RepoMirage, a two-stage evaluation suite built on SWE-Bench Verified.<n>RepoMirage-Perturb applies three types of semantics-preserving repository-level perturbations, revealing a clear performance drop when correct solving requires broader context access.<n>We propose RepoAnchor, a structure-first prototype workflow that separates repository exploration from downstream problem solving.
arXiv Detail & Related papers (2026-05-25T06:26:43Z) - Meta-Agent: From Task Descriptions to Verified Multi-Agent Systems [36.485119937780524]
We present Meta-Agent, a two-phase framework that automatically constructs and executes specialized multi-agent systems.<n>We introduce a three-level error attribution mechanism that distinguishes local, upstream, and structural failures.<n>Experiments against strong multi-agent baselines and ablation studies demonstrate consistent improvements in task success rate, error recovery, and workflow stability.
arXiv Detail & Related papers (2026-05-24T19:38:54Z) - Code as Agent Harness [107.31925305395957]
In emerging agentic systems, code is no longer only a target output.<n>Code increasingly serves as an operational substrate for agent reasoning, acting, environment modeling, and execution-based verification.<n>This survey provides a unified roadmap toward executable, verifiable, and stateful AI agent systems.
arXiv Detail & Related papers (2026-05-18T17:59:03Z) - 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) - MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair [10.709112246848058]
Modern software ecosystems face a rapidly growing number of disclosed vulnerabilities.<n>Most existing systems treat vulnerability repair as a single generation step over the currently visible code context.<n>We present MemRepair, a memoryaugmented agentic framework that formulates vulnerability repair as an iterative, experience-driven process.
arXiv Detail & Related papers (2026-05-17T13:29:46Z) - Dynamic analysis enhances issue resolution [53.50448142467294]
DAIRA (Dynamic Analysis-enhanced Issue Resolution Agent) is an automated repair framework that embeds dynamic analysis into the agent's reasoning cycle.<n> Driven by a Test Tracing-Driven methodology, DAIRA utilizes lightweight monitors to extract critical runtime data.<n>When powered by Gemini 3 Flash Preview, DAIRA establishes a new state-of-the-art (SOTA) performance, achieving a 79.4% resolution rate on the SWE-bench Verified dataset.
arXiv Detail & Related papers (2026-03-23T14:48:54Z) - Architecture-Aware Multi-Design Generation for Repository-Level Feature Addition [53.50448142467294]
RAIM is a multi-design and architecture-aware framework for repository-level feature addition.<n>It shifts away from linear patching by generating multiple diverse implementation designs.<n>Experiments on the NoCode-bench Verified dataset demonstrate that RAIM establishes a new state-of-the-art performance.
arXiv Detail & Related papers (2026-03-02T12:50:40Z) - Co-RedTeam: Orchestrated Security Discovery and Exploitation with LLM Agents [57.49020237126194]
Large language models (LLMs) have shown promise in assisting cybersecurity tasks, yet existing approaches struggle with automatic vulnerability discovery and exploitation.<n>We propose Co-RedTeam, a security-aware multi-agent framework designed to mirror real-world red-teaming.<n>Co-RedTeam decomposes vulnerability analysis into coordinated discovery and exploitation stages, enabling agents to plan, execute, validate, and refine actions.
arXiv Detail & Related papers (2026-02-02T14:38:45Z) - Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework [15.17681731375364]
SeCuRepair is a semantics-aligned, curriculum-driven, and reasoning-enhanced framework for vulnerability repair.<n>At its core, SeCuRepair adopts a reason-then-edit paradigm, requiring the model to articulate why and how a vulnerability should be fixed.<n>SeCuRepair also moves beyond traditional supervised fine-tuning and employs semantics-aware reinforcement learning.
arXiv Detail & Related papers (2025-10-01T15:09:27Z)
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.