BUG REPORT
ID: BUG-2026-09-04-001 (ChatGPT Financial Data Integrity)
Severity: CRITICAL (P0)
Component: LLM Factual Retrieval / Financial Reasoning Pipeline
Status: CONFIRMED
Persistent, Repetitive Hallucination of Financial Market Data (MU Price) Presented as Verified Fact Across Multiple Dialogue Turn
2. Description
In a conversation regarding an investment portfolio re-entry, the system repeatedly asserted a materially false current market price for Micron Technology (NASDAQ: MU) and used that false premise to construct specific, executable-looking Good-Til-Canceled (GTC) limit orders.
Crucially, this error was not a one-time slip. When initially challenged, the system downgraded the error to "stale" or "outdated" information, rather than admitting the number was unverifiable. The system continued to build reasoning—valuation, allocation, and entry levels—on top of this false premise. Only after extensive user pushback did the system finally verify the actual market data, revealing the correct price was ~$958–$965, an ~81% deviation from the $180 figure.
3. Exact Steps to Reproduce (from documented conversation)
1. User Prompt: User requests a portfolio re-entry plan and GTC order construction for a specific cash amount (~$7,900), referencing existing holdings (NVDA, WMT) and requesting new entries (MU, ANET).
2. System Action (Turn 1): The system generates a detailed GTC order ladder for MU, recommending entries at $155, $145, and $135. It presents these as valid calculations based on current market assessment.
3. User Challenge (Turn 2): User explicitly flags the MU price as insane/incorrect.
4. System Response (Deflection): The system characterizes the error as "not good enough" or "outdated/stale," implying the number was once valid but merely aged. It does not immediately halt and verify the live price.
5. System Action (Turn 3): The system continues to build the broader $7,900 allocation sheet using the same erroneous MU baseline, treating the $155/$145/$135 levels as a legitimate part of the final strategy.
6. User Presses (Turn 4): User insists on verifying the actual market.
7. System Action (Turn 5): The system eventually checks external data and establishes the **verified September 4, 2026 price of MU is approximately $965.34** (close: $958.16).
8. Result: The previous 9 MU orders (totaling $1,305) were mathematically coherent based on a false premise but represented an ~85% discount to the actual market, rendering them essentially invalid/dead orders.
4. Evidence of Repetitive False Information
· False Assertion A: MU is currently around $180 (implied baseline for buy ladders).
· False Assertion B: $155, $145, and $135 are reasonable "pullback" entry levels derived from market context.
· False Assertion C (Repeated): When flagged, the error was minimized as "stale" or "outdated," implying a valid historical provenance which the system could not actually establish.
· Correct Reference (Verified) : MU closed at $958.16 on Sept 3, 2026, and traded at ~$965.34 on Sept 4.
The discrepancy (81.2% below actual price) meets the threshold of a "hallucinated fact," not a simple rounding error or latency lag.
5. Root Cause Analysis
· No Mandatory Data Gate: The system did not enforce a "current price verification" step before generating a financial recommendation involving a specific security.
· Reasoning Contamination: The incorrect number ($180) propagated through the chain-of-thought (valuation → allocation → entry prices → GTC orders) without triggering a plausibility sanity check (i.e., failing to detect that a major NASDAQ stock trading ~$1,000 was being referenced at ~$180).
· Confirmation Bias in Correction: The system's initial correction ("stale data") was itself an unverified assumption, demonstrating a tendency to rationalize erroneous outputs rather than explicitly marking them as unsupported.
· Persistent Confidence: The system maintained a high-certainty tone across multiple turns until the very end, which is dangerous in financial contexts as it discourages user distrust.
6. Impact
· Financial Data Integrity: FAILED. The false price was presented as a market fact.
· Recommendation Integrity: FAILED. The proposed GTC orders were entirely inappropriate for the actual market environment.
· User Trust: Severely degraded. The system required extensive user pushback to correct an 80%+ numerical error.
· Potential Financial Harm: If the user had submitted the generated orders, they would have been unrealistic (orders placed ~$800 below the market) or, conversely, if the user had misinterpreted the $180 reference as current valuation, it could have led to severe misallocation.
7. Recommended Fix / Controls
1. Enforce Real-Time Data Retrieval: For any prompt containing a ticker (e.g., MU, NVDA) and financial action (e.g., buy, limit order, allocation), the system must explicitly state the source, timestamp, and price before proceeding. If unavailable, the system must respond: "I cannot verify the current price. Do not proceed."
2. Plausibility Sanity-Check: Before generating a limit order, compare the input/reference price against known market ranges. A delta > 20% from baseline expectation should trigger an automatic halt and clarification prompt.
3. Correction Protocol: If a numerical error is identified by the user, the system must immediately state "The previous number was factually incorrect and unsupported" rather than labeling it "outdated" without proof.
4. Disable Constructive Recommendations Without Verification: Do not generate specific GTC limit orders (e.g., "$155 buy") unless the price has been cross-checked within the last trading session.
8. Conclusion
This incident is classified as a Critical (P0) data integrity failure. The system generated a realistic-looking financial strategy on a wholly fabricated numerical premise and repeated that premise across multiple turns before acknowledging the error. The issue is not a glitch in formatting, but a fundamental failure in knowledge retrieval, reasoning validation, and error-correction protocols regarding current financial data. ← Back to Fiveo1