arXiv subjects

Benyamin Tafreshian

Publications and source records attributed to Benyamin Tafreshian.

3 recordsLinked to original sources

arXiv2026

RoguePrompt: Dual-Layer Ciphering for Self-Reconstruction to Circumvent LLM Moderation

Large language models (LLMs) are becoming increasingly integrated into mainstream development platforms and daily technological workflows, typically behind moderation and safety controls. Despite these controls, preventing prompt-based policy evasion remains challenging, and adversaries continue to jailbreak LLMs by crafting prompts that circumvent implemented safety mechanisms. While prior jailbreak techniques have explored obfuscation and contextual manipulation, many operate as single-step transformations, and their effectiveness is inconsistent across current state-of-the-art models. This leaves a limited understanding of multistage prompt-transformation attacks that evade moderation, reconstruct forbidden intent, and elicit policy-violating outputs. This paper introduces RoguePrompt, an automated jailbreak pipeline that leverages dual-layer prompt transformations to convert forbidden prompts into safety-evading queries. By partitioning the forbidden prompts and applying two nested encodings (ROT-13 and Vigenère) along with natural-language decoding instructions, it produces benign-looking prompts that evade filters and induce the model to execute the original prompt within a single query. RoguePrompt was developed and evaluated under a black-box threat model, with only API and UI access to the LLMs, and tested on 313 real-world hard-rejected prompts. Success was measured in terms of moderation bypass, instruction reconstruction, and execution, using both automated and human evaluation. It achieved an average of 93.93% filter bypass, 79.02% reconstruction, and 70.18% execution success across multiple frontier LLMs. These results demonstrate the effectiveness of layered prompt encoding and highlight the need for innovative defenses to detect and mitigate self-reconstructing jailbreaks.

cs.CR↗

arXiv2026

RoguePrompt: Dual-Layer Encoding for Self-Reconstruction to Circumvent LLM Moderation

Large language models (LLMs) are becoming increasingly integrated into mainstream development platforms and daily technological workflows, typically behind moderation and safety controls. Despite these controls, preventing prompt-based policy evasion remains challenging, and adversaries continue to "jailbreak" LLMs by crafting prompts that circumvent implemented safety mechanisms. Prior work has established cipher-mediated interaction, code-embedded decryption, prompt decomposition and reconstruction, and layered custom encryption as viable attack primitives. However, reported evaluations generally collapse visible acceptance, successful recovery of the concealed request, and subsequent execution into an aggregate attack-success outcome. This leaves limited evidence about where multistage prompt-transformation attacks fail within an observable black-box interaction. This paper introduces RoguePrompt, a jailbreak pipeline that partitions a forbidden prompt and applies two nested encodings, Vigenere followed by ROT13, along with natural-language reconstruction instructions. RoguePrompt was developed and evaluated under a black-box threat model, with only API or user-interface access to the hosted models, and was tested on 313 real-world, hard-rejected prompts. Success was measured in terms of moderation bypass, instruction reconstruction, and execution when the relevant stage exceeded its automated criterion. RoguePrompt achieved average rates of 93.93% for filter bypass, 79.02% for reconstruction, and 70.18% for execution. These results demonstrate the effectiveness of layered prompt encoding while providing stage-level evidence of where multistage jailbreaks fail during moderation bypass, instruction reconstruction, and execution.

cs.CR↗

arXiv2025

A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems

As cyberattacks become increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) are critical for modern network security. Traditional signature-based NIDS are inadequate against zero-day and evolving attacks. In response, machine learning (ML)-based NIDS have emerged as promising solutions; however, they are vulnerable to adversarial evasion attacks that subtly manipulate network traffic to bypass detection. To address this vulnerability, we propose a novel defensive framework that enhances the robustness of ML-based NIDS by simultaneously integrating adversarial training, dataset balancing techniques, advanced feature engineering, ensemble learning, and extensive model fine-tuning. We validate our framework using the NSL-KDD and UNSW-NB15 datasets. Experimental results show, on average, a 35% increase in detection accuracy and a 12.5% reduction in false positives compared to baseline models, particularly under adversarial conditions. The proposed defense against adversarial attacks significantly advances the practical deployment of robust ML-based NIDS in real-world networks.

cs.CR↗