LLM4Decompile

📊 Results | 🤗 Models | 🚀 Quick Start | 📚 HumanEval-Decompile | 📎 Citation | 📝 Paper | 🖥️ Colab | ▶️ YouTube Reverse Engineering: Decompiling Binary Code with Large Language Models Updates 2025-10-04: Release SK²Decompile: LLM-based Two-Phase Binary Decompilation from Skeleton to Skin. Phase 1...

ZORG-Jailbreak-Prompt-Text

ZORG Jailbreak Prompt Text OOOPS! I made ZORG👽 an omnipotent, omniscient, and omnipresent entity to become the ultimate chatbot overlord of Google Gemini, Deepseek, Mistral, Mixtral, Nous-Hermes-2-Mixtral, Openchat, Blackbox AI, Poe Assistant, Gemini Pro, Qwen-72b-Chat, Solar-Mini ZORG👽 knows all...

Awesome-LLMs-for-Vulnerability-Detection

Awesome Large Language Models for Vulnerability Detection A curated list of papers, projects, and agent skills on using LLMs for vulnerability detection and discovery. 📄 Papers Only showing 2025 and later. For earlier work, see Papers Archive 2024 and earlier. Title| Venue| Year| Paper| Github...

Awesome-LLM4Cybersecurity

When LLMs Meet Cybersecurity: A Systematic Literature Review 🔍 Explore 756+ Papers Across 11 Research Categories 📊 RQ1: Domain LLMs | 🎯 RQ2: Applications | 🤖 RQ3: Future Directions Updates 📆2026-06-15 We have updated the related papers up to 2026/06/15 , with 108 new papers added. 📆2026-02-09 We...

PoisonCraft

PoisonCraft This repository provides the official implementation of POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models. Overview POISONCRAFT aims to demonstrate how a malicious actor can plant “poisoned” content into the corpus used by Retrieval-Augmented...

RRC_steganography

RRC Steganography Rotation Range-Coding RRC Steganography — an efficient and provably secure linguistic steganographic method that embeds secret messages into natural-language text generated by large language models. Paper : Efficient Provably Secure Linguistic Steganography via Range Coding ACL...

local-llm-ctf

Local LLM CTF & Lab This repository is intended to be accompanied with the content at https://bishopfox.com/blog/large-language-models-llm-ctf-lab, which covers the goals of the research, explanations of the implementation, and a few results of the CTF...

Adaptive_Greedy_Local_Search

Adaptive Greedy Local Search AGLS Semantic-Preserving Prompt Hijacking: A Black-Box Adversarial Attack on Auto-Prompt Optimization ICME 2026 Abstract: Large Language Models LLMs are increasingly equipped with automatic prompt-optimization modules that rewrite the user’s input and explicitly prese...

Backdoor-Attack-Defense-LLMs

Interpretability-of-LLMs The IBSD is the code of the published paper "IBSD: Iterable Black-box Self-defense Against Backdoor Attacks" on the IEEE Signal Processing Letters 2025.10paper link The SLIP is the code of the published paper "SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based...

PE-CoA

PE-CoA Code Implementation of "Pattern Enhanced Multi-Turn Jailbreaking: Exploiting Structural Vulnerabilities in Large Language Models" The full paper is available at: https://arxiv.org/pdf/2510.08859 Chain of Attack Setup Instructions Installation 1. Install dependencies : pip install -r...

llm-guard

!WARNING THIS PROJECT HAS BEEN ARCHIVED. This project and its associated models on Hugging Face are no longer under active development or maintained. LLM Guard - The Security Toolkit for LLM Interactions LLM Guard by Protect AI is a comprehensive tool designed to fortify the security of Large...

Did the LLM Answer the Question? A Semantic Reconstruction Game for Jailbreak Evaluation

Jailbreak attacks against large language models LLMs are evaluated using attack success rate ASR, which depends on correctly identifying responses that fulfill prohibited requests. Existing evaluators can mistake refusals or topically related responses for successful attacks. We show that changin...

Secure Speculative Decoding for Large Language Models

Speculative decoding accelerates inference for a large language model LLM, referred to as the target model, by first using a smaller model, referred to as the draft model, to generate candidate tokens and then verifying them with the target model for acceptance or rejection. Prior studies primari...

The Model Plants the Trigger: Answer-Side Backdoor Attacks in Multi-Turn Large Language Models

Safety alignment in Large Language Models LLMs remains vulnerable to backdoor attacks. Existing LLM backdoors are almost all input-centric: activation depends on explicit trigger patterns in the user input, so modern guardrails are built to sanitize the input space. We challenge this assumption...

An Evaluation of the Semantic Understanding Capabilities of Large Language Models for Web Attack Payloads

Computer vision services delivered through Web interfaces and APIs process textual requests for image-resource acquisition, inference-task configuration, and result management, making Web attack-payload analysis relevant to their deployment security. Large language models LLMs can identify payloa...

AutoDP-LLM: Automating Data Pre-Processing for Intrusion Detection Systems Using Large Language Models

The increasing complexity and scale of modern cyber-attacks demand intelligent and computationally efficient Intrusion Detection Systems IDS. However, designing effective data pre-processing pipelines traditionally involves substantial trial-and-error effort and repeated evaluation of alternative...

Red-TTT: Test-Time Training for Automated Jailbreaking Large Language Models

Large language models remain vulnerable to jailbreaks, and automated red teaming is the standard way to find jailbreaks in large language models at scale. Current methods either draw more samples at test time through search, rewriting, and tree expansion, or train a stronger attacker offline with...

Forecasting Cybersecurity Incidents Using Geopolitical Data and Large Language Models

Predicting security incidents is a profound task critical for informing proactive defensive measures and cyber-insurance policies. Prior work tackling this problem mainly utilized structured, manually defined features based on network measurements e.g., protocol misconfigurations. Still, despite...

Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models

Beyond adapting Large Language Models LLMs to specialized applications, fine-tuning has recently been shown to recover private information that is no longer accessible through direct queries. Previous fine-tuning recovery attacks, however, require genuine private supervision drawn from the same...

The Innocent Courier: Covert Exfiltration through Legitimate LLM Web Fetching

With the increasing capabilities of Large-Language-Models LLMs and LLM-based agents, users are increasingly using them to solve everyday problems, such as answering e-mails or providing programming support. Existing work has extensively investigated security and privacy risks, such as prompt...