Cogent 发布 VR-1.1,LiveCVEBench 修复通过率达 57.9%
Cogent Reports VR-1.1 Passing Repairs on 57.9% of LiveCVEBench Tasks
Cogent Security 于 2026 年 11 月 3 日推出漏洞修复模型 VR-1.1,在 LiveCVEBench 的 190 项任务中取得 57.9% 的通过修复率,Claude Code 与 Codex 各为 45.3%。该基准覆盖 14 种编程语言、153 个仓库,要求补丁同时通过漏洞检查与功能检查。Cogent 称结果来自安全修复训练和先复现漏洞再打补丁并检测绕过的流程,同时提示基线模型的训练截止时间晚于相关 CVE 公布时间,且生产部署仍需在目标环境中审查测试。
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VR-1.1 在 LiveCVEBench 上的修复通过率高于 Claude Code 与 Codex,读者可据此了解漏洞修复模型的评测口径与局限。
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Cogent Security introduced VR-1.1 on November 3, 2026, reporting that the vulnerability-repair model produced passing repairs for 57.9% of LiveCVEBench’s 190 tasks, compared with 45.3% each for Claude Code and Codex.
The latest addition to Cogent’s VR family extends the remediation capabilities introduced with VR-1. Its evaluation compared VR-1.1 with Claude Code running Claude Opus 5.5 and Codex running GPT-5.6-Cyber. Each system received the same inputs and setup and three attempts per task, with scores based on the final patch after its internal repair attempts.
LiveCVEBench focuses on fixing real-world vulnerabilities in development environments. Its CVE-Factory paper describes a continuously updated set of 190 tasks spanning 14 programming languages and 153 repositories. The benchmark project places agents in real development environments where they must explore codebases autonomously, establish the context of each vulnerability and implement a fix. New CVEs are added regularly.
Cogent’s setup supplied a vulnerability report and affected codebase for each task. A patch counted only when vulnerability checks passed and functionality checks continued to pass. The tasks cover injection, access-control, configuration and memory-safety flaws, so a convincing explanation is insufficient if the change leaves another route to the flaw, disables legitimate behavior, causes a regression or introduces new exposure.
That dual requirement distinguishes a repair from merely stopping one demonstration of an attack. A model must preserve intended behavior while closing the weakness, including related paths that share it.
VR-1.1’S Repair Workflow
Cogent attributes its result to security-repair training and a harness that reproduces a weakness, patches it and then tests the patch for bypasses. Where the run configuration permits, VR-1.1 inspects the report and source code before writing a proof of concept that triggers the flaw. That gives the model a failure to trace and a test to rerun after patching.
The model looks for related code paths sharing the weakness before making a focused change. A separate review in a fresh context examines the patch for bypasses and introduced bugs. VR-1.1 also writes regression tests. When validation fails, its earlier analysis and information about the failure guide another attempt.
A Fiber Repair in Practice
One recorded run involved CVE-2025-48075 in the Fiber web framework. The project’s security advisory says a negative index in nested form data can panic its BodyParser and crash a server, creating a denial-of-service risk. It lists versions from 2.52.6 as affected and 2.52.7 as the patched release.
VR-1.1 reproduced the crash and added a bounds check before the index was used, rejecting negative or excessive indices while preserving valid form handling. Cogent reports that all nine vulnerability checks passed afterward, versus zero before, while all 12 functionality checks passed both before and after. The example came from a separate recorded 182-task run, not the final 190-task evaluation.
Evaluation Limits and Production Use
The evaluation blocked outbound network access during repair and withheld LiveCVEBench’s grading tests until each agent finished. Those conditions prevented retrieval of an upstream fix or direct use of the grading tests while patching.
Cogent cautions that both baseline models have training cutoffs later than the publication dates of the CVEs in the current LiveCVEBench release, meaning their advisories and fixes could have appeared in training data. It identifies the user-report-style task descriptions, blocked networking and identical conditions as limits on that advantage.
In Cogent’s spot checks, models could describe some vulnerabilities from memory but did not reproduce exact patches. The company is also building a task set from CVEs disclosed after the relevant cutoffs and plans to keep it current.
LiveCVEBench tests known vulnerabilities in isolated environments, and passing means satisfying the supplied checks. Cogent says production deployment still requires review and testing in the destination environment. It is bringing VR-1.1 into its remediation workflow, with each patch accompanied by its diagnosis, proof of concept and tests for reviewer inspection. The benchmark result therefore measures checked repairs under controlled conditions; production use still depends on validating those repairs where the software will run.