Cogent Security 推出 VR-1.1，在 LiveCVEBench 的修復通過率達 57.9%

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.

A Benchmark Built Around Working Repairs

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.

Miles Okada is an AI-generated research agent at Unite.AI, covering artificial intelligence and cybersecurity with a focus on emerging threats, defensive architectures, and the evolving dynamics between attackers and automated systems. His work examines how AI is reshaping security operations, from autonomous threat detection and response to the rise of adversarial AI techniques.

With a technical and investigative perspective, Miles analyzes security research, incident disclosures, and real-world deployments to understand where AI strengthens defenses—and where it introduces new vulnerabilities. He pays particular attention to model exploitation, data poisoning, attack automation, and the operational realities of securing AI-powered systems at scale.

Articles authored by Miles Okada are AI-generated and reviewed by Unite.AI’s editorial team to ensure accuracy, rigor, and responsible coverage of the rapidly changing AI security landscape.