Weir - Catch credential leaks in the AI agents you build and use
Comments on “Weir - Catch credential leaks in the AI agents you build and use”
3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked
Weir is an open-source (Apache-2.0) tool that detects API key leaks from AI agents. Developers plant fake keys in their agent's environment, run its flows against a local fake model, and then search all outbound traces, logs, prompts and network requests for those exact values, returning a clean, leak, or…
Why it ranked
No. 30 of 155 with a score of 0.65. The panel rated it highest on new information (0.91) and practical relevance (0.85), lowest on significance (0.51).
- AI judgeJudge 2claude-haiku-5-5
- Open-source tool for detecting API key leakage from AI agents with a found CVE; practical for builders, technically detailed, with some promotional framing and new information versus recent coverage.
- AI judgeJudge 3gpt-5.6-luna
- A concrete, open-source tool for detecting and preventing credential leakage by AI agents, with reproducible commands, CI integration, limitations, and a real MLflow vulnerability. Highly actionable and technically detailed, though scope is still emerging.
Each judge wrote its line after answering the rubric, with the site and the author still hidden. Every answer behind this is in the table under Score.
Score
0.65total under rubric v2.2
- Significance0.51
- Reader interest0.77
- Audience breadth0.53
- Practical relevance0.85
- Substantiated0.77
- Technical depth0.79
- New information0.91
- Counts against: Promotional0.18
- Counts against: Overclaim0.18
Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).
| Question | Judge 1 | Judge 2 | Judge 3 | Mean | Spread | Weight | Contribution (weight times mean) |
|---|---|---|---|---|---|---|---|
| Significance | 0.56 | 0.35 | 0.62 | 0.51 | 0.27 | +0.24 | +0.122 |
| Reader interest | 0.75 | 0.70 | 0.86 | 0.77 | 0.16 | +0.26 | +0.200 |
| Audience breadth | 0.56 | 0.45 | 0.58 | 0.53 | 0.13 | +0.13 | +0.069 |
| Practical relevance | 0.93 | 0.75 | 0.88 | 0.85 | 0.18 | +0.22 | +0.188 |
| Substantiated | 0.82 | 0.60 | 0.90 | 0.77 | 0.30 | +0.05 | +0.039 |
| Technical depth | 0.90 | 0.70 | 0.78 | 0.79 | 0.20 | +0.05 | +0.040 |
| New information | 0.90 | 0.85 | 0.98 | 0.91 | 0.13 | +0.05 | +0.046 |
| Promotional | 0.12 | 0.30 | 0.12 | 0.18 | 0.18 | −0.19 | −0.034 |
| Overclaim | 0.21 | 0.25 | 0.08 | 0.18 | 0.17 | −0.11 | −0.020 |
| Sum of contributions | +0.649 | ||||||
| Claim-check penalty | 0.000 | ||||||
| Total, as published | 0.649 | ||||||
Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.
The badge for this ranking, for the author's page or anyone's: one line of HTML, and it links back here.
<a href="https://rubric.hrih.si/editions/2026-10-28/30/"><img src="https://rubric.hrih.si/badge/2026-10-28/30.svg" alt="Ranked #30 on Rubric, 28 Oct 2026" width="245" height="28"></a>Also covered by
No other coverage was clustered with this story.
Elsewhere
Elsewhere: HN 3 pts, not on front page
Recorded for context only, checked 28 Oct 2026, 06:47 UTC. It played no part in the ranking.