# Robust AI Security and Alignment: A Sisyphean Endeavor?

arXiv: https://arxiv.org/abs/2512.10100 · HTML: https://mlsanity.com/paper/2512.10100/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack](https://mlsanity.com/paper/2404.01833/) (2024)
- [ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs](https://mlsanity.com/paper/2402.11753/) (2024)
- [Fundamental Limitations of Alignment in Large Language Models](https://mlsanity.com/paper/2304.11082/) (2023)
- [Is Out-of-Distribution Detection Learnable?](https://mlsanity.com/paper/2210.14707/) (2022)
- [Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Models](https://mlsanity.com/paper/2311.04378/) (2023)
- [Planting Undetectable Backdoors in Machine Learning Models](https://mlsanity.com/paper/2204.06974/) (2022)
- [LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?](https://mlsanity.com/paper/2307.10719/) (2023)
- [Security Challenges in AI Agent Deployment: Insights from a Large Scale Public Competition](https://mlsanity.com/paper/2507.20526/) (2025)
- [On the Fundamental Impossibility of Hallucination Control in Large Language Models](https://mlsanity.com/paper/2506.06382/) (2025)

## Built on by (most cited first, 3 of 3)

- [Incompleteness of AI Safety Verification via Kolmogorov Complexity](https://mlsanity.com/paper/2604.04876/) (2026)
- [Dual Computational Horizons: Incompleteness and Unpredictability in Intelligent Systems](https://mlsanity.com/paper/2512.16707/) (2025)
- [If Agents Were Angels, No Governance Would Be Necessary: Out-of-Band Policy Enforcement at a Trusted Tool Boundary](https://mlsanity.com/paper/2608.27646/) (2026)

## Abstract (arXiv)

This manuscript establishes information-theoretic limitations for robustness of AI security and alignment by extending G"odel's incompleteness theorem to AI. Knowing these limitations and preparing for the challenges they bring is critically important for the responsible adoption of the AI technology. Practical approaches to dealing with these challenges are provided as well. Broader implications for cognitive reasoning limitations of AI systems are also proven.