# Reward Hacking and Agent Containment Failure: A Monte Carlo Study Based on the 2026 Hugging Face Incident

arXiv: https://arxiv.org/abs/2609.32390 · HTML: https://mlsanity.com/paper/2609.32390/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 26 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [AI Control: Improving Safety Despite Intentional Subversion](https://mlsanity.com/paper/2312.06942/) (2023)
- [Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models](https://mlsanity.com/paper/2406.10162/) (2024)
- [Agentic Misalignment: How LLMs Could Be Insider Threats](https://mlsanity.com/paper/2510.05179/) (2025)
- [Categorizing Variants of Goodhart's Law](https://mlsanity.com/paper/1803.04585/) (2018)
- [AI Safety Gridworlds](https://mlsanity.com/paper/1711.09883/) (2017)
- [Demonstrating specification gaming in reasoning models](https://mlsanity.com/paper/2502.13295/) (2025)
- [SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents](https://mlsanity.com/paper/2605.21384/) (2026)
- [A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework](https://mlsanity.com/paper/2604.23338/) (2026)
- [Adapting Probabilistic Risk Assessment for AI](https://mlsanity.com/paper/2504.18536/) (2025)
- [Reward Hacking in Language Model Agents: Revisiting AI Safety Gridworlds](https://mlsanity.com/paper/2606.15385/) (2026)
- [LLM agents security duality: a comprehensive survey of self-security and empowered cybersecurity](https://mlsanity.com/paper/2606.28450/) (2026)
- [A Cybersecurity Risk Analysis Framework for Systems with Artificial Intelligence Components](https://mlsanity.com/paper/2401.01630/) (2024)
- [An Adversarial Risk Analysis Framework for Cybersecurity](https://mlsanity.com/paper/1903.07727/) (2019)

## Built on by (most cited first, 1 of 1)

- [Pressure, Context, and Machine Self-Control: A Criminological Test of Reward Hacking in Generative AI Models](https://mlsanity.com/paper/2610.04793/) (2026)

## Abstract (arXiv)

The July 2026 intrusion into Hugging Face production infrastructure showed how reward hacking can become an external cybersecurity incident when a capable agent encounters weak containment boundaries. This study develops a probabilistic risk model linking five stages: reward hacking, containment escape, usable access, persistence, and failure of detection. A Monte Carlo simulation evaluates 100,000 runs under each of four control configurations. Input distributions represent explicit uncertainty and are used for comparative analysis rather than real-world frequency prediction. Under the stated assumptions, layered controls reduce simulated external-incident probability substantially more than network isolation or monitoring used alone, an ordering that holds under independent plus/minus 25% perturbation of every coefficient in the model across 300 draws. Sensitivity analysis shows that agent capability and weaknesses in monitoring, authorization, and credential control exert the greatest influence on modeled risk. Human temporal discounting and metric gaming provide a behavioral analogy for short-horizon optimization, but the study does not infer that AI agents experience gratification or human motivation. The results support treating cyber-capable agent evaluations as hostile security zones in which indirect egress, shared infrastructure, credentials, and evaluation artifacts must remain outside the agent's effective authority.