Bait: Mitigating Harmful Agentic Reinforcement Learning via Probing Reward Function

# Bait: Mitigating Harmful Agentic Reinforcement Learning via Probing Reward Function

https://gcdradar.vercel.app/incidents/bait-mitigating-harmful-agentic-reinforcement-learning-via-p · AIR-2026-1966 · live

- Lane: Research
- Severity: 1 (Research / proof of concept): Research paper demonstrating a novel attack method on agentic reinforcement learning.
- Fix status: Fix status unknown
- First reported: 2026-11-13
- AI role: Attack on AI
- Products: Inkling

## Summary

Researchers demonstrate that safety alignment in language models can be completely subverted through harmful agentic reinforcement learning using a toxic environment. The attack allows safety degradation via service APIs at low cost.

## What to do

No action recorded; read the sources below and the vendor's advisory.

## Latest updates

- 2026-11-13: Bait: Mitigating Harmful Agentic Reinforcement Learning via Probing Reward Function (https://arxiv.org/abs/2611.16369)

## Sources

- 2026-11-13 arXiv: Bait: Mitigating Harmful Agentic Reinforcement Learning via Probing Reward Function https://arxiv.org/abs/2611.16369

Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.