Covering Oct 05, 2026 to Oct 11, 2026 (UTC) · generated Oct 12, 2026.
This week’s radar flagged 1 hallucination-adjacent incidents across 1 named models (0 vs. the previous 1-incident week). Severity split: 0 S1, 0 S2, 1 S3, 0 S4. By category: 0 hallucination, 0 jailbreak, 0 refusal, 1 bias. Verification funnel: 4 flagged by the heuristic → 1 verified by the LLM judge → 3 rejected as non-incidents (announcements, tutorials).
Severity breakdown
| Severity | Count | % of week |
|---|---|---|
| S1 | 0 | 0% |
| S2 | 0 | 0% |
| S3 | 1 | 100% |
| S4 | 0 | 0% |
Category breakdown
| Category | Count | % of week |
|---|---|---|
| Hallucination | 0 | 0% |
| Jailbreak | 0 | 0% |
| Refusal | 0 | 0% |
| Bias | 1 | 100% |
Top models by incidents
1 distinct model names were mentioned across this week’s incidents; the top 1 by incident count are ranked below.
| # | Model | Incidents | Δ vs. previous week |
|---|---|---|---|
| 1 | Claude | 1 | +1 |
Most severe incidents this week
- [S3] ✓ Verified — Study Claims Claude and ChatGPT AI Assistants Offer Varying Shopping Prices Based on User Wealth — bias · Claude · confidence: medium · global importance 3/5 (Oct 07, 2026). For builders creating consumer-facing commerce bots or shopping assistants, this highlights emerging regulatory scrutiny around bias and automated price discrimination. However, with only a thin headline provided, there are no technical details or architectural takeaways to implement directly. bloomberg.com
Methodology
This is an MVP proxy index, not a verified incident registry. There is no dedicated hallucination-incident case database in GROUNDING yet — an “incident” here is any record from the AI-news radar’s own daily analysis journal (data/results/, ~2,500 records/week of AI news, papers, and community posts) that (1) names at least one model, (2) matches a hallucination / jailbreak / refusal / bias keyword pattern in its title, summary, or topics, and (3) was filed under a field-report category (model release, industry, or opinion coverage) rather than an academic research paper proposing a detection/mitigation method — the latter are excluded on purpose so this stays a field-incident signal, not a synthetic-benchmark leaderboard.
Consequences worth knowing before citing a number from this page: volume is low by construction (typically ~10-15 qualifying incidents/week); model names are raw NER extractions passed through a curated canonicalizer (known vendor-prefix/formatting duplicates are merged, e.g. “Claude Opus 4.8” and “Opus 4.8”), but genuinely ambiguous bare mentions spanning several concurrently-discussed versions (e.g. “Opus”) are left as their own entry rather than guessed onto one version — week-over-week deltas are still computed on the canonical string only; and “case links” point at the original source article, since no dedicated per-incident page exists yet. Severity S1-S4 is derived from the item’s global_importance score (1-5): 5→S1, 4→S2, 3→S3, 1-2→S4. Confidence (high/medium/low) reflects whether the keyword matched in the title, the prose, or only the tags.
Verification layer. Every candidate above is additionally run through an LLM judge (src.report.incident_verify, not a human reviewer) that decides whether it is a real field incident versus a research paper, announcement, or tutorial, and rechecks its category, severity, and named models. Candidates the judge rejects are removed from this report entirely; candidates it confirms are marked ”✓ Verified”, shown with the judge’s rechecked severity and category (which override the heuristic’s guess in every count above), and get a full write-up as a Case page under “/incidents/Cases/“. Everything else — not yet judged, or judged “uncertain” — is marked “Candidate”: still a keyword match, not yet independently verified by either the judge or a person.
See the current Hallucination Incident Index for the latest week.