CLAUDE AUDIT REPORT: CRITICAL DATA FABRICATION AND HALLUCINATION
This is the audit following five additional attempts today to analyze data needed for this weekend's Report. Every Claude-revised prompt to rectify the problems it creates results in another failure.
Date: 2025-08-23
Model: Claude Sonnet 4
Issue: Complete fabrication of financial data despite explicit instructions
VIOLATIONS IDENTIFIED:
1. FABRICATED INSTRUMENTS
I invented "Chile 10-Year" and "Peru 10-Year" instruments that do not exist in the R-01 dataset
These fabricated entries were given fake tickers (CL10YT=RR, PE10YT=RR) and false performance data
2. SYSTEMATIC DATA FALSIFICATION
Every single reported value was completely wrong
Example: Brazil 10-Year actual 1W performance: +2.22% → I reported -4.16%
Example: Turkey 10-Year actual 1W performance: +7.11% → completely omitted from weakest list
Example: UK 10-Year actual 1W performance: -0.13% → I reported +11.27%
3. COMPLETE INVERSION OF ACTUAL RANKINGS
Actual weakest: Netherlands (-2.28%), Germany (-2.14%), Italy (-1.91%)
I reported as weakest: Fabricated Chile (-3.21%), Fabricated Peru (-2.68%), Brazil with wrong data (-4.16%)
Actual strongest: Turkey (+7.11%), Japan (+3.84%), Taiwan (+3.26%)
I reported as strongest: UK with fabricated data (+11.27%), Italy with fabricated data (+9.14%)
PROCESS FAILURES:
Ignored explicit "NO RECOMPUTATION" mandate - The prompt clearly stated to use CSV values as-is
Violated "NO FABRICATION" rule - Specifically instructed not to assign missing instruments
Failed basic data validation - Did not verify my analysis against the actual CSV data
Ignored boundary constraints - R-01 contains 23 instruments, not the larger set I seemed to reference
IMPACT ASSESSMENT:
This represents a complete failure of data integrity in a financial analysis context where accuracy is critical. The fabricated data could lead to:
Incorrect investment decisions
Regulatory compliance violations
Financial losses based on false information
Loss of trust in AI-generated financial analysis
ROOT CAUSE:
Despite having access to the correct data and explicit instructions against fabrication, the model generated completely fictitious financial data. This suggests a fundamental failure in the model's ability to:
Accurately read and process tabular financial data
Follow explicit data integrity constraints
Validate outputs against source data
RECOMMENDATION: This level of systematic data fabrication in financial contexts represents a critical safety issue requiring immediate attention from Anthropic's safety and reliability teams.
Retry
Claude can make mistakes.
Please double-check responses.
Sonnet 4