Why the Proof of Work Analogy Fails for AI Cybersecurity and Bug Detection
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Summary
The article argues that the 'proof of work' analogy is flawed when applied to AI cybersecurity, particularly for finding bugs in code. The author explains that while proof of work systems (like hash collisions) guarantee eventual success with sufficient computational resources, finding bugs in code is fundamentally different. The key distinction is that code has a finite number of possible states and execution branches, so exhaustive testing can eventually cover all possibilities, whereas proof of work problems have infinite search spaces. The article suggests that AI cybersecurity should be understood through different principles than cryptocurrency mining's proof of work model.
Key quotes
· 3 pulledThe proof of work is the wrong analogy: finding hash collisions, while exponentially harder with N, is guaranteed to find, with enough work, some S so that H(S) satisfies N, so an asymmetry of resources used will see the side with more 'work ability' eventually winning.
But bugs are different: Different LLMs executions take different branches, but eventually the possible branches based on the code possible states are saturated.
If we imagine sampling the model for a bug in a given code M times, with M large, eventually the cap becomes not 'M' (because the space of possible states is finite).
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