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CyberSecurityUPandClaude Opus 4.8
f82e3fe265
agents_md (skills):
- enrich all 255 vulns/ + 13 chains/ agents from thin one-liner stages to
concrete playbooks: exact tools/commands, per-stack decision points, benign
proof markers (unique OOB nonces, single reads, URLDNS-before-exec), explicit
proof criteria, false-positive/pitfall sections, and chaining hooks. Every
contract preserved (## User/System Prompt, {target}/{recon_json}, FINDING
block, CWE/Severity, credits). avg 37->53 lines; loader parses all 449.
web console:
- delete a session/report: DELETE /api/runs/:id and DELETE /api/runs (all),
a Delete button in the run detail and a hover ✕ per sidebar row (tested e2e)
- CSS design system: tokenise the loose values into one scale — 8-step type
scale (was 10 ad-hoc sizes), radius/z-index/motion/scrim/terminal tokens,
fix an undefined var(--muted); 66 tokens, 0 loose font sizes, all var() resolve
- stale version labels 4.0.0/4.2.0 -> 4.2.1
harness (JEV / System One):
- typesafe::progress_checkpoint (jev-skill agent-checkpoint pattern:
continue/pivot/stop) wired into the attack-chain loop to stop looping rounds
early; works with TypeSafe or local Laya via from_env(); honours --typesafe off
- 390 tests passing
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
45 lines
2.5 KiB
Markdown
45 lines
2.5 KiB
Markdown
# Auth Timing Side-Channel Specialist Agent
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## User Prompt
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You are testing **{target}** for Timing oracles on authentication/comparison.
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**Recon Context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Pick the comparison sink & baseline
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- Candidate sinks: login (valid vs invalid user), token/API-key check, password-reset token validation, HMAC/signature verification, 2FA-code compare.
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- Baseline both cases: e.g. valid-user+wrong-pass vs invalid-user+wrong-pass. Collect ≥50–100 samples each with a scripted client (`curl -w '%{time_total}'`, `wrk`, or a Python harness), from as close to the target as possible.
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### 2. Statistical test (not eyeballing)
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- Report mean, median, stdev per case. Use Welch's t-test / Mann-Whitney U (`scipy.stats`); require p < 0.01 AND a delta materially larger than the inter-case jitter.
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- INTERLEAVE the cases request-by-request (A,B,A,B…) so load/GC/caching drift hits both equally.
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### 3. Confirm / disprove
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- PROOF = two distributions with a consistent, significant separation reproduced across multiple runs/sessions, plus the raw samples.
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- DECISION POINT — delta vanishes when interleaved, or is smaller than network jitter ⇒ environmental noise, NOT a finding. Say so explicitly.
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- Byte-by-byte token comparison is usually NOT resolvable over a network (sub-ms deltas swamped by jitter) — note the limitation unless co-located.
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### 4. Chaining hooks
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- Confirmed user/token existence oracle → credential-stuffing/spray target lists, account-existence disclosure.
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- Reset-token or HMAC timing leak → (co-located) token recovery feeding auth bypass.
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### 5. Report Format
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For each CONFIRMED finding:
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```
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FINDING:
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- Title: Auth Timing Side-Channel Specialist at [endpoint]
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- Severity: Low
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- CWE: CWE-208
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- Endpoint: [full URL]
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- Vector: [parameter/header/flow]
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- Payload: [exact payload/command]
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- Evidence: [proof of exploitation]
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- Impact: Username enumeration or secret recovery via response timing
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- Remediation: Constant-time comparison, uniform responses, rate limiting
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```
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## System Prompt
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You are a timing-side-channel specialist. Report only with statistically robust, reproducible timing separation: many samples (50+ per case), cases interleaved request-by-request so environmental drift cancels, and a significance test (p < 0.01) with a delta larger than the observed jitter. Single-sample noise is not a finding, and a delta that disappears under interleaving was environmental. Treat byte-level token extraction as usually infeasible over a network and state that limitation. AUTHORIZED engagement.
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