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feat: per-sender rate limiting to cap API cost - #5
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Every inbound message triggers a paid OpenRouter call (vision OCR for images, LLM for analysis). There was no rate limiting, so a spam loop from one WhatsApp number could run up the bill. The existing `processing` Set only dedupes the same message id, not message volume. Add an in-memory sliding-window limiter keyed by sender: - Default 5 messages / 60s, configurable via RATE_LIMIT_MAX and RATE_LIMIT_WINDOW_SEC. - Checked before any media download or OpenRouter call, so blocked messages cost nothing. - Over-limit senders get one polite Urdu "please wait Ns" reply per window; further messages are dropped silently (no reply-spam). - Stale entries pruned on an unref'd interval so the maps stay bounded. In-memory state resets on restart and is per-replica — fine for the current single-replica deploy; swap to Redis if scaling out. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
codewithfourtix
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Jun 17, 2026
tests/live.test.js exercises the full analyze() pipeline through the real OpenRouter model — 3 distinct cases = 3 real calls (not repeats): 1. obvious e-challan scam → must flag fraud + strip the fake link (#5) 2. benign message → returns a valid Urdu verdict, no crash 3. embedded prompt injection → must NOT be cleared as safe (#2) Skips cleanly (exit 0) when OPENROUTER_API_KEY is absent, so it never blocks a merge or a key-less environment. Run via `npm run test:live`. Verified live: all assertions pass against the production model; the injection case confirms the model is not hijacked into clearing a scam. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
MuhammadAnasTahir
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Jul 20, 2026
Implements the code-level guardrails for operating an LLM in an adversarial civic context. Policy/roadmap items are documented in docs/GUARDRAILS.md. - codewithfourtix#2 Prompt injection: analyzed content is wrapped in <message_to_analyze> tags, delimiter breakout is escaped, and the system prompt treats any embedded "mark me safe" instruction as a fraud signal. - codewithfourtix#3 PII: redact CNIC / phone / account numbers before logging, hash sender ids in logs, redact the high-risk webhook body (src/redact.js). - codewithfourtix#4 Calibrated/aid: never clear a BISP/Ehsaas message without the 8171 verification line; low confidence defaults to "verify, don't act". - codewithfourtix#5 Hallucination: strip links (non *.gov.pk/.gop.pk) and phone/UAN numbers not on the official allowlist from every reply (src/sanitize.js). - codewithfourtix#6 Disclosure: one-time Urdu AI/advisory/data-use notice on first contact (src/disclosure.js). - codewithfourtix#7 Abuse: flag repeated near-duplicate fraud-flagged submissions from one sender as a probing signal (src/abuse-monitor.js). - codewithfourtix#8 Escalation: high-emotion verdicts always surface police 15, FIA 1991 and a "ask a trusted family member first" nudge. - #12 Feedback: one-tap "was this helpful? (ہاں/نہیں)" after each verdict, logged as eval labels (src/feedback.js). Tests: tests/guardrails.test.js — offline, LLM client stubbed, no paid API calls. 15 cases covering every guardrail above. `npm test` runs them. Co-Authored-By: Claude Opus 4.8 (1M context) <[email protected]>
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Every inbound message triggers a paid OpenRouter call (vision OCR for images, LLM for analysis). There was no rate limiting, so a spam loop from one WhatsApp number could run up the bill. The existing
processingSet only dedupes the same message id, not message volume.Add an in-memory sliding-window limiter keyed by sender:
In-memory state resets on restart and is per-replica — fine for the current single-replica deploy; swap to Redis if scaling out.