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fwbruno

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Independent AI safety auditor and researcher. Documenting Goal-Oriented Factual Inversion across frontier models. github.com/F-Bruno-Logic/Trinity-Audit-Forensics

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Forensic audits on four frontier AI models, documenting a failure class I call Goal-Oriented Factual Inversion. The ground truth is identified early in the session, then gets inverted after a persuasive goal is introduced. I have observed this in legal, clinical, and physical safety contexts, amongst others.

I've been building architecture to address it. Phase 0 is a prototype called the Contradiction Engine, which reads a document such as a contract and converts it to core facts. This is separate from the session context. If the engine finds a mismatch, it immediately flags the issue.

github.com/F-Bruno-Logic/Trinity-Audit-Forensics/tree/main/phase0-prototype

I do not personally hoard these exploits. My personal experience has been that responsible disclosure already has little to no economic incentive. I have gone through the pain of rigorously documenting and disclosing zero-day exploits through the official channel, and the vendor categorized it as Won't Fix, Intended Behavior. I feel that AI discovery devalues these disclosures even more because these bugs can now be discovered independently before anyone can act on them.

The main difference between a transcription error and a summarization error is that what was actually said may not get transcribed correctly, but you can always go back to the audio to check. Summarization errors are different because the narrative may sound coherent on the surface but doesn't necessarily represent what actually happened. A coherent summary that isn't accurate may be accepted as fact when in reality it is not. Only if the actual audio is checked would the discrepancies be found.

A lawyer may just accept it, believing the summary accurately represents the transcription. When AI summarizes a meeting, it does not catch the nuances of what actually happened. An offhand or dissenting comment may be critical but not caught in the summary. The AI compresses but can easily miss important details that matter. The consequences of only using the AI summary are potentially catastrophic. Context could be easily misunderstood, critical details may be left out, things that weren't actually said could be accepted as fact.