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Get Started Free →Use when reviewing error handling middleware, API route handlers, or server responses for security-sensitive information disclosure.
.claude/skills/thedaviddias-stack-trace-exposure/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 14 |
| gemini-3.1-pro-preview | 100% | 1 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -13% | 0% |
| case-14 | ✓→✓ | = Same ✓ | -7% | 0% |
| case-01 | ✓→✓ | = Same ✓ | -35% | 0% |
| case-02 | ✓→✓ | = Same ✓ | -7% | 0% |
| case-03 | ✓→✓ | = Same ✓ | -2% | 0% |
Stack traces reveal file paths, function names, library versions, and sometimes database schema or configuration details. An attacker uses this information to identify the exact version of a framework or ORM, look up known CVEs for that version, and craft a targeted exploit. OWASP lists "Security Logging and Monitoring Failures" (A09) as a top-10 risk partly because organisations often expose this information without realising it.
Check whether production API error responses include stack traces, file paths, or internal implementation details.
Implement a central error handler that logs full details server-side and returns only a sanitised, generic error message to the client.
Explain what information stack traces reveal and how attackers use that information to identify and exploit vulnerabilities.
Review error handlers, catch blocks, and API response code. Flag any location where error.stack, error.message (raw), or internal paths are serialised directly into a response body.
For full implementation details, code examples, and framework-specific guidance, see references/rule.md.
Rule page: https://frontendchecklist.io/en/rules/security/stack-trace-exposure
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-14 | pass→pass | 13,833 | 10,280 | -26% | 1 | 1 | 0% | 2,239 | 2,086 | -7% | 0 | 0 | — |
case-01 | pass→pass | 12,190 | 12,864 | +6% | 1 | 1 | 0% | 2,275 | 1,473 | -35% | 0 | 0 | — |
case-02 | pass→pass | 19,214 | 14,511 | -24% | 1 | 1 | 0% | 3,457 | 3,232 | -7% | 0 | 0 | — |
case-03 | pass→pass | 12,131 | 9,297 | -23% | 1 | 1 | 0% | 2,216 | 2,176 | -2% | 0 | 0 | — |
case-04 | fail→pass | 10,949 | 8,299 | -24% | 1 | 1 | 0% | 1,941 | 1,695 | -13% | 0 | 0 | — |
case-05 | pass→pass | 10,566 | 10,854 | +3% | 1 | 1 | 0% | 2,155 | 2,389 | +11% | 0 | 0 | — |
case-06 | pass→pass | 14,375 | 10,700 | -26% | 1 | 1 | 0% | 2,522 | 2,330 | -8% | 0 | 0 | — |
case-07 | pass→pass | 12,752 | 11,357 | -11% | 1 | 1 | 0% | 2,470 | 2,459 | -0% | 0 | 0 | — |
case-13 | pass→pass | 6,754 | 6,821 | +1% | 1 | 1 | 0% | 1,331 | 1,694 | +27% | 0 | 0 | — |
case-08 | pass→pass | 8,960 | 7,381 | -18% | 1 | 1 | 0% | 1,620 | 1,573 | -3% | 0 | 0 | — |
case-09 | pass→pass | 14,243 | 10,517 | -26% | 1 | 1 | 0% | 2,602 | 2,297 | -12% | 0 | 0 | — |
case-10 | pass→pass | 13,011 | 12,180 | -6% | 1 | 1 | 0% | 2,455 | 2,566 | +5% | 0 | 0 | — |
case-11 | pass→pass | 11,577 | 10,106 | -13% | 1 | 1 | 0% | 2,120 | 2,103 | -1% | 0 | 0 | — |
case-12 | pass→pass | 11,183 | 8,833 | -21% | 1 | 1 | 0% | 2,251 | 2,019 | -10% | 0 | 0 | — |
case-15 | pass→pass | 12,731 | 12,327 | -3% | 1 | 1 | 0% | 2,384 | 2,684 | +13% | 0 | 0 | — |
case-16 | pass→pass | 14,293 | 13,071 | -9% | 1 | 1 | 0% | 2,644 | 2,544 | -4% | 0 | 0 | — |
case-17 | pass→pass | 11,769 | 7,743 | -34% | 1 | 1 | 0% | 2,075 | 1,764 | -15% | 0 | 0 | — |
case-18 | pass→pass | 9,463 | 8,703 | -8% | 1 | 1 | 0% | 1,615 | 1,798 | +11% | 0 | 0 | — |
case-19 | pass→pass | 11,853 | 9,160 | -23% | 1 | 1 | 0% | 2,156 | 2,033 | -6% | 0 | 0 | — |
case-20 | pass→pass | 8,829 | 6,383 | -28% | 1 | 1 | 0% | 1,697 | 1,533 | -10% | 0 | 0 | — |
case-21 | pass→pass | 12,946 | 10,827 | -16% | 1 | 1 | 0% | 2,489 | 2,472 | -1% | 0 | 0 | — |
case-22 | pass→pass | 14,156 | 9,795 | -31% | 1 | 1 | 0% | 2,559 | 2,064 | -19% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.