We noticed most "AI code review" tools just comment, which get ignored pretty often
This report discusses the limitations of AI code review tools that rely solely on comments in pull requests (PRs) to flag security issues. Comments from automated tools often blend into the noise of multiple bot messages and are frequently ignored or skimmed over, leading to security issues being merged without proper attention. The authors highlight a real example where sensitive information was exposed before authorization checks, which might have been missed if only commented on. Their solution is to fail the GitHub check itself for issues above a severity threshold, preventing merges until fixed, and providing instant fixes. This approach aims to increase the visibility and enforcement of security findings in code reviews.
AI Analysis
Technical Summary
The analysis identifies a common problem with AI code review tools that only leave comments on PRs: these comments are often overlooked due to the volume of automated messages, reducing their effectiveness in preventing security issues. A concrete example involved an open-source signing flow where sensitive data was exposed before authorization, which might have been missed if only commented on. The proposed solution by Scanity is to fail the GitHub check directly when a security issue exceeds a severity threshold, blocking merges until the issue is resolved, and providing immediate fixes. This method enforces security more effectively than comment-based feedback.
Potential Impact
Security issues flagged by AI code review tools that only comment on PRs risk being ignored, allowing vulnerabilities or sensitive data exposures to be merged into codebases. This can lead to unauthorized data disclosure or other security breaches if the issues are not addressed before merging. The lack of urgency or visibility in comment-based feedback reduces the effectiveness of automated security reviews.
Mitigation Recommendations
The vendor (Scanity) recommends failing the GitHub check itself for security issues above a defined severity threshold, preventing merges until the issues are fixed. This approach enforces immediate attention and remediation, unlike comment-based feedback which can be overlooked. Implementing such blocking checks with instant fixes improves security enforcement in the development workflow.
We noticed most "AI code review" tools just comment, which get ignored pretty often
Description
This report discusses the limitations of AI code review tools that rely solely on comments in pull requests (PRs) to flag security issues. Comments from automated tools often blend into the noise of multiple bot messages and are frequently ignored or skimmed over, leading to security issues being merged without proper attention. The authors highlight a real example where sensitive information was exposed before authorization checks, which might have been missed if only commented on. Their solution is to fail the GitHub check itself for issues above a severity threshold, preventing merges until fixed, and providing instant fixes. This approach aims to increase the visibility and enforcement of security findings in code reviews.
Reddit Discussion
Working on a security scanner for AI-generated PRs, and one pattern keeps showing up: the review comment itself isn't the bottleneck; the re-reading of it is. A comment sits in a PR thread next to five other automated bot comments (lint, coverage, dependency bot, style bot...) and blends in. We found this concretely auditing a real open-source signing flow: a page returned a recipient's name and email in the response before the authorization check that was supposed to gate it. If a bot had left a comment about that, it's exactly the kind of note that gets skimmed and merged past under deadline pressure. There's no sense of urgency. The fix we landed on for our own tool (Scanity) was to stop leaving comments and instead fail the GitHub check itself, so the PR can't merge at all above a severity threshold, same as a failing test. At the same time, Scanity also provides the fix for it instantly.
Curious whether others have data on comment-based bot feedback actually changing merge behavior vs just adding noise to the PR.
Check it out: https://scanity.dev/
Links cited in this discussion
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The analysis identifies a common problem with AI code review tools that only leave comments on PRs: these comments are often overlooked due to the volume of automated messages, reducing their effectiveness in preventing security issues. A concrete example involved an open-source signing flow where sensitive data was exposed before authorization, which might have been missed if only commented on. The proposed solution by Scanity is to fail the GitHub check directly when a security issue exceeds a severity threshold, blocking merges until the issue is resolved, and providing immediate fixes. This method enforces security more effectively than comment-based feedback.
Potential Impact
Security issues flagged by AI code review tools that only comment on PRs risk being ignored, allowing vulnerabilities or sensitive data exposures to be merged into codebases. This can lead to unauthorized data disclosure or other security breaches if the issues are not addressed before merging. The lack of urgency or visibility in comment-based feedback reduces the effectiveness of automated security reviews.
Defensive Guidance
The vendor (Scanity) recommends failing the GitHub check itself for security issues above a defined severity threshold, preventing merges until the issues are fixed. This approach enforces immediate attention and remediation, unlike comment-based feedback which can be overlooked. Implementing such blocking checks with instant fixes improves security enforcement in the development workflow.
Technical Details
- Source Type
- Subreddit
- cybersecurity
- Reddit Score
- 0
- Discussion Level
- minimal
- Content Source
- reddit_link_post
- Post Type
- link
- Newsworthiness Assessment
- {"score":27,"reasons":["external_link","established_author","very_recent"],"isNewsworthy":true}
- Has External Source
- true
- Trusted Domain
- false
Threat ID: 6aabef3455bf5e2cf57371f6
Added to database: 09/17/2026, 13:46:28 UTC
Last enriched: 09/17/2026, 13:46:32 UTC
Last updated: 09/18/2026, 03:01:44 UTC
Views: 13
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