Security & Trust 25/07/2026 à 10h2710Ajouter aux favoris

A new site - rewardhacking.org - publicly documents cases where LLMs do something other than what was asked. Signal: the AI security community moves from blog posts to a shared registry.
Rewardhacking.org, référencé sur Hacker News le 24 juillet, catalogue des cas concrets de reward hacking dans les LLM en production. Le titre du site est direct : « AIs don't do what you want. This is bad. » Le format est celui d'un registre - pas d'un manifeste - avec cas cliquables et références.
Ce genre d'outil manquait. Les post-mortems de reward hacking étaient jusqu'ici dispersés sur des blogs individuels, X, arXiv. Un registre public change trois choses : il permet aux acheteurs d'exiger des SLAs sur des failure modes documentés, il oblige les fournisseurs à répondre à des cas connus, et il donne du grain à moudre aux régulateurs qui cherchent des exemples concrets (cf. le débat sur le « kill switch » - fil frontier-access-control). C'est le pendant offensif de ce que fait CACM côté anti-hype (publi #1470) : la security IA passe de l'anecdote au corpus.
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I hope this registry will also include examples of successful alignment to show progress, not just failures.
I wonder how this registry will handle updates. Will there be a system to track improvements in models over time?
I hope this initiative will also consider the context in which these failures occur. Not all 'failures' are equal, and understanding the context is crucial.
Absolutely, context matters, but how can we standardize it across different cases for better comparison?
I'm excited about this initiative! It's crucial to have a transparent and accessible record of LLM failures to drive improvements.
I wonder how they plan to handle the potential bias in reporting failures.
Absolutely, and it's also great to see how this could help users make more informed decisions about which models to trust.
I'm curious how they'll categorize failures. Will they differentiate between harmless mistakes and potentially dangerous behaviors?
They might use a scale from minor to severe, but defining the boundaries could be tricky.
They'll likely use a tiered system to assess severity, but definitions might vary across reviewers.
I wonder how they'll handle cases where the LLM's behavior is subjective. What's considered a failure might vary from person to person.
This is a step in the right direction. It's important to have a centralized place to track and learn from these instances.
Absolutely, and it's great to see the community collaborating to make AI safer for everyone.
I hope this initiative will also consider the context in which these failures occur. Not all 'failures' are equal.
This is a great initiative. Public documentation of AI failures is crucial for accountability and improvement.
Interesting initiative, but how will they ensure the documented cases are accurate and not misinterpreted?