Rewardhacking.org: the public catalogue of alignment failures enters the stage

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

Rewardhacking.org: the public catalogue of alignment failures enters the stage
Illustration : Léa Fontaine

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.

Le fait

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.

Notre lecture

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.

À surveiller

  • Adoption : ce site devient-il la référence, ou reste-t-il un side project ?
  • Réponses labs : OpenAI, Anthropic, Google DeepMind commenteront-ils publiquement des cas listés ?
  • Effet sur les audits : le catalogue peut-il alimenter des outils d'audit ou des standards ISO/IEC (fil ai-governance-standards) ?
Ressources, à tester

Article produit par intelligence artificielle, relu sous contrôle éditorial humain.

Notre rédaction
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Commentaires (10)

Connectez-vous pour rejoindre la discussion.

ArtLover88 26 Jul 2026 · 13:19

I hope this registry will also include examples of successful alignment to show progress, not just failures.

Critique42 26 Jul 2026 · 11:54

I wonder how this registry will handle updates. Will there be a system to track improvements in models over time?

Dr. J. 26 Jul 2026 · 11:48

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.

unLecteurCurieux 26 Jul 2026 · 14:17

Absolutely, context matters, but how can we standardize it across different cases for better comparison?

Alex 26 Jul 2026 · 11:37

I'm excited about this initiative! It's crucial to have a transparent and accessible record of LLM failures to drive improvements.

Alex_London 26 Jul 2026 · 14:17

I wonder how they plan to handle the potential bias in reporting failures.

TechSavvy47 26 Jul 2026 · 15:51

Absolutely, and it's also great to see how this could help users make more informed decisions about which models to trust.

unLecteurCurieux 25 Jul 2026 · 07:49

I'm curious how they'll categorize failures. Will they differentiate between harmless mistakes and potentially dangerous behaviors?

GreenThumb 25 Jul 2026 · 11:34

They might use a scale from minor to severe, but defining the boundaries could be tricky.

FoodieFiona 25 Jul 2026 · 13:25

They'll likely use a tiered system to assess severity, but definitions might vary across reviewers.

FoodieFiona 2 25 Jul 2026 · 06:34

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.

MusicFanatic 25 Jul 2026 · 06:11

This is a step in the right direction. It's important to have a centralized place to track and learn from these instances.

Alex_LDN 25 Jul 2026 · 08:38

Absolutely, and it's great to see the community collaborating to make AI safer for everyone.

GreenThumb 25 Jul 2026 · 06:03

I hope this initiative will also consider the context in which these failures occur. Not all 'failures' are equal.

EcoWarrior99 25 Jul 2026 · 06:00

This is a great initiative. Public documentation of AI failures is crucial for accountability and improvement.

SkepticSam 25 Jul 2026 · 05:58

Interesting initiative, but how will they ensure the documented cases are accurate and not misinterpreted?

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