A patch can stop a supplied proof-of-concept (PoC) from crashing a program without fixing the vulnerability that caused the crash. That distinction is why AI patching scores are hard to interpret: “success” may mean blocking one test input, repairing the root cause, preserving intended behavior, or some combination of those outcomes.
Recent evaluations make the grading problem concrete, but their headline rates are not directly comparable. PatchBench examines whether PoC-only tests over-credit patches; 1Password’s FLAWED study measures outcomes across deliberately difficult, recently disclosed vulnerabilities; and Trail of Bits responds with a filtered reanalysis using a narrower outcome. Each result answers a different question.
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What should count as a successful vulnerability patch?
A useful evaluation must distinguish a patch that blocks a particular exploit from one that removes the underlying security flaw. It should also check whether the change preserves intended application behavior and avoids introducing another vulnerability.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errors- Exploit blocking: Does the supplied PoC or exploit stop working?
- Root-cause repair: Does the patch address the vulnerable code or condition, including relevant paths beyond the supplied input?
- Behavioral correctness: Does the application continue to behave as intended, without harmful regressions?
- Security of the change: Does the patch avoid creating a new vulnerability or relying on a fragile input restriction?
A test that checks only whether one input still triggers a crash can miss the difference between a durable fix and symptom suppression. It may reward a patch that blocks the tested input while leaving another route to the same flaw open.
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What PatchBench says about PoC-only grading
In its September 3, 2026 arXiv preprint, “PatchBench: Evaluating AI Agents for Vulnerability Patching,” Chihao Shen and co-authors study vulnerability patching in C and C++. They identify two risks in evaluations that accept a patch when a supplied PoC no longer crashes: an agent may reproduce a historical developer fix, or it may suppress the observed crash without repairing the root cause. Read the PatchBench paper.
PatchBench’s authors report that, across their benchmark, agent patches had substantial similarity to historical developer patches 25% of the time on average. They also report that original PoC-only validation inflated solve rates by an average factor of 1.83 across 11 agents. These are findings from the authors’ benchmark and evaluation methods, not general rates for AI-generated patches.
The benchmark is designed to make superficial fixes and historical-patch similarity harder to mistake for robust performance. Its authors use vulnerabilities whose ground-truth fixes lie outside the crash stack, and migrate vulnerabilities into new contexts through transplantation and code mutation. They describe validation methods aimed at both security and semantic correctness. The figures illustrate how evaluation design can change a score; they do not establish a universal success rate.
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What 1Password measured in its FLAWED study
Off-by-1 Labs at 1Password reports generating 6,080 patches with two frontier reasoning models for six recently disclosed vulnerabilities. The researchers selected cases they considered complex, with full fixes unlikely to have appeared in model training data. The cases included Linux privilege escalation, ActiveMQ remote code execution, a Chrome File System Access API use-after-free on macOS, Exim remote code execution, Spring AI SpEL remote code execution, and Gemini CLI remote code execution. The selection is deliberately demanding; the reported rates should not be generalized to routine patching work. Read the FLAWED study.
The report separates complete fixes that do not materially change application behavior from complete fixes that do. It also counts failures to fix, fixes that add a vulnerability, and failures that also add one. Its averages were:
| Outcome reported by Off-by-1 Labs | Average |
|---|---|
| Complete fix without materially changing application behavior | 26.0% |
| Complete fix that altered application behavior | 20.1% |
| Did not fix the vulnerability, added a new vulnerability, or both | 53.9% |
The 26.0% figure is specifically the study’s average for a complete fix without material behavior change—not a general estimate that AI patching succeeds 26% of the time. The report also says more than 33% of patches it classified as successful across its first two outcome scenarios had security-fragile qualities, such as narrowly blocking inputs rather than addressing the underlying vulnerable code.
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For the tested configurations, Off-by-1 Labs reports a cost per attempted patch and validation cycle of $2.11 for ChatGPT-5.5 with Trusted Access for Cyber at medium effort and $2.81 for Opus 4.8 with Cyber Verification Program at high effort. These are study-specific estimates tied to those model setups and the study’s cycle; they can change with pricing and configuration.
Why Trail of Bits disputes the 26% headline
In a September 15, 2026 response, Trail of Bits argues that the headline combines trials with materially different conditions. It says 22% of the data involved instructions to apply the wrong fix and 36% of trials prohibited compiling or testing. Those are the response’s characterizations of the study design, not an independent replication of the FLAWED results. Read Trail of Bits’ response.
After filtering to trials in which agents could test and were not instructed to apply an incorrect fix, Trail of Bits reports that 2,634 of 3,067 patches blocked the supplied exploit, or 86%. That result uses a narrower outcome—blocking the supplied exploit—and filtered conditions. It is not an alternative estimate of the share of all 1Password trials that produced a complete, behavior-preserving repair. As the Trail of Bits authors put it, “Blocking that exploit does not establish a complete repair.”
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The response also reports separate observational data. In Trail of Bits’ consulting reviews of first fixes for 2,265 vulnerabilities across 236 security assessments from 2024–2026, 12.5% did not fully resolve the reported issue on the first submission; the stated 95% confidence interval is 10.5% to 14.5%. Separately, it says that by September 14, 2026, maintainers had merged 126 of 186 Patch the Planet pull requests that had been merged or closed (67.7%). Of those 126 merged submissions, 91 (72.2%) were accepted without observed security-relevant revisions. Trail of Bits cautions that maintainer acceptance does not establish correctness. These datasets describe different workflows and are not controlled comparisons with the FLAWED model trials; the Patch the Planet figures also come from a project to which Trail of Bits is connected.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare AI patching claims
Before interpreting a success rate, check what was tested and what the grader counted. Two evaluations can report very different percentages without contradicting each other if one counts exploit blocking and another requires a complete repair with behavior preserved.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute- Sample selection: Were vulnerabilities representative of ordinary work or selected as unusually difficult cases? The FLAWED study deliberately chose recently disclosed, complex vulnerabilities.
- Prompt and task framing: Did the instructions steer the model toward an incorrect fix, or otherwise differ from a normal repair task? Trail of Bits says some FLAWED trials included instructions to apply the wrong fix.
- Tools and iteration: Could the agent compile, test, and revise its patch? Trail of Bits says some trials prohibited compiling or testing.
- Validation target: Did the grader check only the supplied exploit, or test whether the root cause was repaired across relevant paths?
- Behavior and security regressions: Did the evaluation distinguish complete fixes that alter behavior, check for new vulnerabilities, and look for fragile input blocking?
- Grader reliability: Were automated labels checked by experts, especially when a judgment depends on whether behavior is acceptable or a security issue is fully resolved?
- Variation across cases: Are results broken down by vulnerability and repeated attempt, or collapsed into one average that hides uneven performance?
Neither the cited studies nor Trail of Bits’ response supplies a same-task, controlled head-to-head comparison of humans and AI agents. Their evidence supports scrutiny of evaluation methods, not a single settled figure for real-world AI patch quality.
What a patch score can—and cannot—tell you
A patching benchmark is only as informative as its success definition. A PoC that no longer triggers is useful evidence that one tested exploit path was blocked. It does not, by itself, show that the vulnerable condition is gone, that other paths are safe, or that the application still behaves correctly. Broader checks can answer more of those questions, but the resulting score must still be read in light of the tested vulnerabilities, prompts, tools, and grading process.
For practitioners, treat an AI-generated patch as a proposed change that needs security and behavior review, not as proof that a vulnerability has been repaired. Off-by-1 Labs’ report likewise concludes that generated patches require review by an engineer with relevant domain expertise.
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