pulledpork

PulledPork PulledPork for Snort and Suricata rule management from Google code Find us on Libera.Chat IRC pulledpork Copyright C 2009-2021 JJ Cummings, Michael Shirk and the PulledPork Team! Thank you for choosing to use PulledPork! This file provides some basic guidance on the usage of PulledPork...

scodescanner

SCodeScanner SCodeScanner stands for Source Code scanner where the user can scans the source code for finding the Critical Vulnerabilities. The main objective for this scanner is to find the vulnerabilities inside the source code before code gets published in Prod. Website for newreleases, new...

steamer

Steamer Import, manage, search public dumps. Do you have massive amounts of CSV, .sql, .txt, that have credentials, passwords, and hashes inside? Use Steamer to manage them! Load them into a MongoDB database, and either use the console directly, or just use the handy web interface complete with...

DeeCLIP

DeeCLIP: AI生成画像を検出するための堅牢で汎化可能なTransformerベースのフレームワーク ☀️ 本研究がお役に立つ場合は、ぜひリポジトリにスターを付け、論文を引用してください！ ☀️ TODO 以下の項目に取り組んでいます： ArXiv論文を公開 学習スクリプトを公開 推論スクリプトを公開 チェックポイントを公開 DeeCLIP DeeCLIP は、CLIP-ViT と 融合学習 を組み合わせた、AI生成画像の検出...

better_opts_attacks

May I have your attention? 打破基于微调的提示注入防御：利用架构感知攻击 本仓库包含运行ASTRA和ASTRA++攻击的代码，这些攻击能够突破SecAlign++、SecAlign、StruQ防御。此外，本仓库还提供了一些生成的攻击示例和攻击日志。 环境搭建 克隆仓库 这将克隆仓库以及所有子模块，并放置到正确的位置。 root@kitploit: git clone --recurse-submodules https://github.com/nishitvp/betteroptsattacks.git 设置Python环境...

fixing-google-secops-detections

Google SecOps Chronicle Curated Detections: Flaw Analysis & Tuning This repository documents architectural design flaws, logic discrepancies, and tuning strategies for native Google SecOps Chronicle Curated Detections. While Google Threat Intelligence GTIG provides exceptional conceptual threat...

Astra Linux – Vulnerability in Linux 5.10, Linux, Linux 5.15

In the Linux kernel, the following vulnerabilities have been resolved: ALSA: hda/ca0132: fixed a buffer overflow issue in tuningctlset The tuningctlset function might cause a buffer overflow if it does not break the loop after matching A. static int tuningctlset... for i = 0; i TUNINGCTLSCOUNT; i...

Important: Red Hat Security Advisory: Red Hat Multiarch Tuning 1.3.4

The 1.3.4 release of the Red Hat Multiarch Tuning Operator. For more details, see product documentation in the references section. Enhancements: With this update, MTO has been updated to use go version 1.26.7. Bug Fixes: Previously, deleting a ClusterPodPlacementConfig object immediately after...

hate_crack v2.36.1

root@kitploit: / | \ / | \ \ | | / \ \ / \ / \ /\ \ \ / | |/ / \ Y // | | \ / \ | | // \ | \ /|| /\ | \ / / // / / / / Installation Installing from source is the only supported path. hatecrack is not distributed on PyPI: pip install hate-crack resolves to a 0.0.0 placeholder that...

Astra Linux – Vulnerability in Linux 6.12

In the Linux kernel, the following vulnerability has been resolved: net/rds: Fixed a circular locking dependency in rdstcptune syzbot reported a circular locking dependency in rdstcptune, where sknetrefcntupgrade is called while holding the socket lock:...

Assessing Automated Prompt Injection Attacks in Agentic Environments

Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effective for jailbreaking--remain underexplored in realistic agentic settings. We present a comprehensive empirical evaluation of automated prompt...

Empirical Evaluation of Large Language Models for Migration of Code Fragments to Post-Quantum Cryptography

The transition to post-quantum cryptography PQC requires not only replacing vulnerable cryptographic primitives, but also refactoring the surrounding software logic. While existing PQC migration frameworks provide organizational guidance, practical code-level remediation remains largely manual an...

Gate AI: LLM Security Benchmark Evaluation Methodology and Results

Published evaluations of prompt-injection and jailbreak detectors for Large Language Models often suffer from two systematic weaknesses: per-dataset threshold tuning and undisclosed operating points. We describe an evaluation harness that addresses both. The detector under evaluation is scored...

Patcher: Post-Hoc Patching of Backdoored Large Language Models

Large language models remain vulnerable to jailbreak backdoor attacks, where adversaries poison safety alignment data to embed hidden triggers that bypass safety mechanisms. Existing defenses often require comprehensive attack information or multiple triggered examples, making them impractical wh...

Detecting Trojaned DNNs Via Spectral Regression Analysis

Modern DNNs are repeatedly fine-tuned to incorporate new data and functionality. This evolutionary workflow introduces a security risk when updated data cannot be fully trusted, as adversaries may implant Trojans during fine-tuning. We present MIST, a Trojan detection approach that analyzes how a...

Backdooring Masked Diffusion Language Models

Masked diffusion language models MDLMs are emerging as a compelling new paradigm for text generation, but their training-time security remains largely unexplored. Existing backdoor attacks on Gaussian diffusion models or autoregressive language models do not directly apply to MDLMs because MDLMs...

DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models

While vision and multimodal foundation models underpin critical tasks from perception to complex reasoning, they remain highly vulnerable to adversarial attacks. However, traditional adversarial attacks are typically limited to single, predefined objectives, tightly coupling each attack to a...

On Fixing Insecure AI-Generated Code through Model Fine-Tuning and Prompting Strategies

The security of AI-generated code remains a major obstacle to its widespread adoption. Although code generation models achieve strong performance on functional benchmarks, their outputs frequently contain bugs and security weaknesses that undermine their trustworthiness. Prior work has explored a...

How Code Representation Shapes False-Positive Dynamics in Cross-Language LLM Vulnerability Detection

How code representation format shapes false positive behaviour in cross-language LLM vulnerability detection remains poorly understood. We systematically vary training intensity and code representation format, comparing raw source text with pruned Abstract Syntax Trees at both training time and...

XekRung Technical Report

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and...