Adversarial Reframing: A Framework for Targeted Generation in Language Models

Computer Science > Cryptography and Security [Submitted on 20 May 2026] Adversarial Reframing: A Framework for Targeted Generation in Language Models Shahnewaz Karim Sakib, Swati Kar, Anindya Bijoy Das Large Language Models (LLMs) are widely deployed in diverse real-world settings, yet remain vulnerable to jailbreaking, where prompt-based attacks bypass safety filters. We present THREAT (Targeted Harmful generation via Reframing and Exploitation of Adversarial Tactics), a reasoning-driven framework that coordinates multiple LLMs in an iterative search loop to find textual jailbreak prompts. We formulate prompt discovery as a nonconvex optimization problem and provide an efficient solution that lowers runtime and improves attack effectiveness. Across diverse datasets and model architectures, THREAT delivers higher attack success rates with lower computational cost than prior methods. The crafted prompts were flagged as harmful in fewer than 1% of cases, compared with about 50% refusals for the corresponding unmodified prompts. These findings reveal previously undetected vulnerabilities in aligned LLMs and position THREAT as a practical tool for proactively strengthening the safety of foundation models. Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2605.21674 [cs.CR]   (or arXiv:2605.21674v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.21674 Focus to learn more Submission history From: Anindya Bijoy Das [view email] [v1] Wed, 20 May 2026 19:31:07 UTC (2,973 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)