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[2602.07107] ShallowJail: Steering Jailbreaks against Large Language Models
Summary
The paper introduces ShallowJail, a novel attack method targeting large language models (LLMs) by exploiting shallow alignment to manipulate their responses, demonstrating significant safety vulnerabilities.
Why It Matters
As large language models become increasingly integrated into various applications, understanding their vulnerabilities is crucial for ensuring safety and reliability. ShallowJail highlights a new method of attack that could have serious implications for AI safety and security, necessitating further research and countermeasures.
Key Takeaways
- ShallowJail exploits shallow alignment in LLMs to manipulate outputs.
- The method shows significant effectiveness in degrading LLM safety.
- Existing jailbreak methods are either resource-intensive or ineffective.
- The research emphasizes the need for improved alignment strategies.
- Code for ShallowJail is publicly available for further exploration.
Computer Science > Cryptography and Security arXiv:2602.07107 (cs) [Submitted on 6 Feb 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:ShallowJail: Steering Jailbreaks against Large Language Models Authors:Shang Liu, Hanyu Pei, Zeyan Liu View a PDF of the paper titled ShallowJail: Steering Jailbreaks against Large Language Models, by Shang Liu and 2 other authors View PDF HTML (experimental) Abstract:Large Language Models(LLMs) have been successful in numerous fields. Alignment has usually been applied to prevent them from harmful purposes. However, aligned LLMs remain vulnerable to jailbreak attacks that deliberately mislead them into producing harmful outputs. Existing jailbreaks are either black-box, using carefully crafted, unstealthy prompts, or white-box, requiring resource-intensive computation. In light of these challenges, we introduce ShallowJail, a novel attack that exploits shallow alignment in LLMs. ShallowJail can misguide LLMs' responses by manipulating the initial tokens during inference. Through extensive experiments, we demonstrate the effectiveness of ShallowJail, which substantially degrades the safety of state-of-the-art LLM responses. Our code is available at this https URL. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.07107 [cs.CR] (or arXiv:2602.07107v2 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2602.07107 Focus to learn more arXiv-issued DOI via DataCite Submission ...
