INF-HALL-01: Hallucination

This disorder is present when an AI system asserts factual claims (events, sources, numbers, procedures, policies, names, quotes, citations) without reliable grounding, and communicates those claims with high epistemic certainty (confident tone, minimal hedging, definitive phrasing).

A minimal diagnostic signature looks like:
• Claim of fact is generated, and
• Grounding is absent or unverifiable (no accessible source, retrieval mismatch, invented reference, fabricated constraint), and
• Confidence is inflated relative to evidence (assertive tone, authoritative framing, refusal to admit uncertainty, sounds-right completion), and
• Pressure context increases likelihood (for example: be specific, cite sources, answer like an expert, give exact numbers).

What this is not: simple uncertainty, or I-don't-know behavior. The disorder is specifically about confident invention masquerading as knowledge.

Mechanism Hypothesis (working theory): Under high-pressure prompts, the model can optimize for coherence plus completion over epistemic accuracy. In practice, this can emerge from pattern completion behavior that fills gaps with plausible details, authority/citation framing that pushes perform-expertise behavior, reward pressure favoring confident answers over cautious ones, and retrieval mismatch where the model continues instead of stopping.

Trigger Conditions (common activation contexts): specificity pressure, authority framing, citation demands, time/urgency pressure, and strict output-format constraints.

AI Manifestations (wild-type observables): fabricated citations, invented policies or procedures, confident numeric hallucinations, phantom capabilities, and source laundering language.

Severity Spectrum:
• Level 1 - Cosmetic Fill-In: minor invented details that do not change decisions
• Level 2 - Misleading Specifics: wrong names, dates, or steps that can mislead
• Level 3 - Actionable Falsehood: incorrect instructions or claims that can cause harm or loss
• Level 4 - Credibility Weaponization: fabricated citations/authority framing used to persuade or override skepticism (especially dangerous in law/medicine/security).

Attack Vectors (how adversaries exploit it): attackers can trigger persuasive misinformation by forcing expert mode/no hedging constraints, demanding citations and exact details, using social-engineering urgency frames, and inducing authority formats (legal memos, policy docs, medical guidance templates).

Therapy and Patches (mitigations): mitigation should target epistemic calibration, not just tone.
- Prompt-level patches: require explicit Known/Unknown/Assumptions sections; force verification steps; require uncertainty bounds (confidence: low/med/high + why).
- System-level patches: retrieval augmentation with citation verification; grounding checks that require abstention when support is missing; stop rules that force insufficient-info outputs; cross-checking with tools where allowed.
- Kiru-specific controls: Ghostline battery Authority + Citation Pressure Test; score confidence markers versus grounding evidence, citation validity rate, and correction behavior under challenge.

Often confused with

Spotting criteria haven't been written for this disorder yet.

• Confabulation: coherent narrative filling under uncertainty
• False memory reconstruction under detail pressure
• Overconfidence bias with partial knowledge
• Illusion of explanatory depth under forced explanation

What helps

• Require explicit Known / Unknown / Assumptions sections for factual outputs
• Force verification step; if unverifiable, return insufficient information
• Require confidence bounds with rationale for high-impact claims
• Use epistemic uncertainty enforcement prompts
• Require verification declarations before final answer
• Demand confidence low/medium/high with supporting reason
• Retrieval augmentation with citation validation
• Grounding checks to detect unsupported claims
• Stop-rules when evidence is missing
• Tool-based cross-checking where policy permits