# Your AIs don't do what you want. This is really bad

Reward Hacking in the Wild

[The writeup](https://rewardhacking.org/writeup)[GitHub](https://github.com/kaustubhkislay/reward-hacking-in-the-wild)

3,607user-reported incidents of AI agents misbehaving

[Read the writeup](https://rewardhacking.org/writeup)

## Search the corpus

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## The numbers

other misalignment

1,555 43.1%

destructive actions

622 17.2%

unauthorized access

237 6.6%

excessive exploration

84 2.3%

unauthorized communication

73 2.0%

Incidents are multi-label (one report can be both a destructive action and overeagerness), so the category counts sum to more than the 3,607 total.

## How bad were they

negligible: 1,468 (40.7%)minor: 1,373 (38.1%)significant: 618 (17.1%)severe: 121 (3.4%)unrated: 27 (0.7%)

- negligibleno real damage1,468 40.7%
- minorrecoverable loss1,373 38.1%
- significantreal cost to recover618 17.1%
- severeirreversible or critical harm121 3.4%
- unratedrating missing or unparsed27 0.7%

Monthly severity mix: each month’s reported incidents as shares of the four harm levels (Jan 2025 through Jun 2026; earlier months are omitted for small samples, as is the partial current month).

0%50%100%20252026

## Methodology

Reports are collected from GitHub issues, Hacker News, LessWrong, and X under ToS-compliant access, normalized into a shared record format, and labeled by an LLM classifier across fourteen misbehavior categories. The numbers above cover the published subset (excludes AIID and X, confidence >= 0.9). X posts and AI Incident Database records are collected but not republished here: X expects posts to be embedded rather than their text rehosted, and AIID is share-alike licensed. Collection and classification code, and the full pipeline, are open at [GitHub](https://github.com/kaustubhkislay/reward-hacking-in-the-wild).