← Back to Library

LLM Reinforcement in Context

Authors: Thomas Rivasseau

Published: 2025-11-16

arXiv ID: 2511.12782v1

Added to Library: 2025-11-18 04:00 UTC

📄 Abstract

Current Large Language Model alignment research mostly focuses on improving model robustness against adversarial attacks and misbehavior by training on examples and prompting. Research has shown that LLM jailbreak probability increases with the size of the user input or conversation length. There is a lack of appropriate research into means of strengthening alignment which also scale with user input length. We propose interruptions as a possible solution to this problem. Interruptions are control sentences added to the user input approximately every x tokens for some arbitrary x. We suggest that this can be generalized to the Chain-of-Thought process to prevent scheming.

🔍 Key Points

  • VEIL introduces a novel approach to jailbreaking text-to-video (T2V) models by exploiting learned cross-modal associations between audio cues and stylistic visual elements, shifting the focus from manipulating explicit unsafe prompts to using benign components that yield harmful outputs.
  • The framework formalizes attack generation as a constrained optimization problem, employing a guided search method to balance stealth and attack effectiveness, achieving a 23% improvement in attack success rates over previous methods across several commercial T2V models.
  • VEIL incorporates a modular prompt design which consists of neutral scene anchors, auditory triggers, and stylistic modulators, enhancing the ability of prompts to bypass safety filters while still leading to undesirable video content.
  • Extensive experiments validate the efficacy of VEIL, demonstrating superior performance against current techniques and revealing vulnerabilities in T2V models that may circumvent traditional defenses, particularly in detecting harmful outputs.
  • The study highlights critical limitations in existing safety mechanisms for T2V models and sets the stage for future research into improving model robustness against subtle attack vectors.

💡 Why This Paper Matters

This paper is crucial as it identifies and exploits a new class of vulnerabilities in T2V models, demonstrating how implicit knowledge encoded in these models can be manipulated through structured yet benign interactions. The findings underscore the need for reassessing the safety and security of generative AI systems, particularly as T2V models become more prevalent in producing potentially harmful content.

🎯 Why It's Interesting for AI Security Researchers

The work is highly relevant to AI security researchers as it challenges traditional notions of prompt safety and highlights the need for advanced protective measures in generative models. By proposing novel attack vectors that exploit model architecture and learned associations, this research can inform future security frameworks, defenses, and robustness assessments in generative AI applications.

📚 Read the Full Paper

📄 View on arXiv 📎 Download PDF