Your AI agent fixed the bug, but what else did it change?

Today we’re publishing patching-bugs and regression-hunt, two plugins that let engineers delegate patch development and regression checks to agents. Our patching-bugs plugin reproduces reported bugs, develops fixes, and tests them. The regression-hunt plugin checks whether any changes break previously working and supported behavior. Engineers can direct the work and review their results; maintainers can supply their own security assumptions and patching requirements, so the agents work within the project’s rules.

We built patching-bugs and regression-hunt through Patch the Planet, our joint initiative with OpenAI, where we’ve co-authored hundreds of security patches for widely used open-source projects. Motivated by our major successes on projects like Scapy, our plugins draw on our analysis of merged patches and thousands of our engineers’ Codex sessions.

The plugins provide maintainers with a structured workflow, evidence, independent checks, and best practices to improve their patching hygiene. In our preliminary evaluations, regression-hunt produces 25% fewer false-positive findings than a baseline Codex agent prompted to “review the PR for regressions.”

Our September post reported regressions introduced by some of our Patch the Planet patches. Since then, we’ve reviewed every patch that a Trail of Bits engineer authored and maintainers merged upstream. The review confirmed 21 regressions from over 130 PRs merged: 16 functional, one performance, and four build/test (see appendix). None broke a security protection.

(“Regression” refers to a new defect introduced by a change that breaks previously correct and supported behavior along a path a real user or attacker can reach.)

For each patch, an agent looked for potential regressions, confirmed each one with a proof-of-concept (PoC) run before and after the change, and then passed the results to an engineer for review. The review was noisy: today’s bug disclosures arrive at an unprecedented speed and scale, and for patching to keep pace, the industry needs to increase its signal-to-noise ratio. The 21 confirmed regressions were identified among 58 candidates in a single run (~36% precision). But because agents take many different approaches and reach different results, each run generates more candidates to manually sort through.

That led us to two conclusions:

Standardizing both steps also makes every run repeatable and comparable. No fixed checklist will catch every regression, so we collect agent trajectories from our engineers’ patching sessions, and use the tasks that an earlier version failed to benchmark for the next one (figure 1).

We used two primary sources to guide the design of our plugins: a review of every patch we merged upstream and the thousands of Codex sessions our engineers used to write those patches.

This led to four principles that shaped our patching-bugs and regression-hunt plugins:

The regression-hunt plugin (Figure 2): In addition to the four principles above, there are two core aspects that are critical to the plugin:

Our review of merged PRs yielded a set of 21 confirmed regressions that we could use to evaluate regression-hunt. We used the whole set to compare the plugin with a baseline Codex agent prompted to “review the PR for regressions.”

Across those runs, regression-hunt produced 25% fewer false-positive findings than the baseline, with a 95% confidence interval of 14% to 38%. Its strict evidence requirements also left some known regressions classified as borderline: the plugin identified a possible regression but did not gather enough evidence to confirm it. Fewer false positives therefore came with a tradeoff in how many known regressions it could confidently report.

Next, we’ll investigate why these cases remained borderline and improve the plugin’s strategies for gathering evidence. Our goal is to confirm more real regressions while giving maintainers clearer information when the evidence remains inconclusive.

The patching-bugs plugin is a distillation of all the best practices we’ve identified by analyzing thousands of Codex sessions by Trail of Bits engineers (Figure 3). In addition to the more traditional capabilities (such as reproduction on live deployments and independent adversarial review), the plugin emphasizes:

Both patching-bugs and regression-hunt will continue to evolve. Our engineers are using them in their Patch the Planet work now, and we’re collecting data on how they’re being used, what works, and what doesn’t. That data, along with better evaluations, will shape the next version’s capabilities:

These plugins encode best practices we’ve found for an agent to patch a bug properly, and they improve when you tell them how your project works.

The plugins work best when you input two kinds of project knowledge:

THREAT_MODEL.md`` PATCHING_GUIDELINES.md (or AGENTS.md)

For our Patch the Planet work with GPAC, our engineers wrote a threat model and a project-specific patching skill that captures how GPAC’s maintainers want bugs reproduced, what evidence a fix needs, and what a patch must preserve. Liboqs and validator.js have also published threat models in accordance with our guidance. The more comprehensive and specific these files are to your project, code conventions, and operating style, the better the output you’ll receive.

As AI accelerates bug discovery, we need to turn those findings into reliable fixes just as quickly. Solutions need to balance speed and quality: an agent needs to understand a report, decide what needs to change, and check that a patch fixes the issue without introducing regressions. Our patching-bugs and regression-hunt plugins bring what we’ve learned from Patch the Planet into that workflow, helping maintainers move from a bug report to a patch they can confidently review and ship.

Install patching-bugs and regression-hunt, add a THREAT_MODEL.md and a PATCHING_GUIDELINES.md to your repository, and put them to work on your next bug report. Give the tools your project’s context so they can help you focus on the right fix and validate it faster. Then share your feedback, including comments about where our defaults don’t fit your project. Those real-world cases will help us build the next version.

This table lists confirmed regressions in our patches, with links and a brief description of what broke.