TID-The-Instant-Destroyer

TID — The Instant Destroyer Independent security research in the field of protection against cache-based side-channel attacks. Overview TID is a specialized protocol designed to eliminate sensitive data remnants such as cryptographic keys from CPU cache layers $L1, L2, \text and L3$ immediately...

NoFrak

NoFrak NoFrak는 Android용 개념 증명Proof of Concept 구현체로, Martin Georgiev, Suman Jana, Vitaly Shmatikov가 작성한 "Breaking and Fixing Origin-Based Access Control in Hybrid Web/Mobile Application Frameworks" NDSS 2014에 설명된 프래킹fracking 공격을 방지하기 위해 설계되었습니다. Cordova Android Cordova Android는 Cordova 기반 프로젝트를...

enhanced-iframe-protection

增强型 iFrame 保护 - 浏览器扩展 增强型 iFrame 保护（EIP）是一款轻量级扩展，用于自动检测嵌入的 iframe 元素并提供详细的警告，以防范浏览器中的浏览器（BITB）攻击。 截图 在上面的截图中，一个钓鱼网站将一个 iframe 元素嵌入到一个经过样式设计、看起来像真实浏览器窗口的 div 中（带有声称来自 accounts.google.com 的虚假 URL 栏）。 该扩展已检测到该 iframe 并显示安全警告，突出显示实际的钓鱼域名为（bigphish.ca）。 安装 Mozilla Firefox Google Chrome Microsoft Edge...

Towards a Unified Misuse Monitoring Benchmark

LLM agents increasingly act in multi-actor environments, exposing them to misuse from multiple sources: decomposition attacks, where a harmful request is split into innocuous sub-requests, and prompt injection attacks, where a compromised tool delivers a malicious instruction. Existing evaluation...

PerSpectron: Detecting Invariant Footprints of Microarchitectural Attacks with Perceptron

Detecting microarchitectural attacks is critical given their proliferation in recent years. Many of these attacks exhibit intrinsic behaviors essential to the nature of their operation, such as creating contention or misspeculation. This study systematically investigates the microarchitectural...

PT-2026-104785

Name of the Vulnerable Software and Affected Versions VillaTheme CURCY woo-multi-currency versions prior to 2.2.18 Description An incorrect calculation issue allows integer attacks, which occur when an application fails to properly handle integer overflows or underflows, potentially leading to...

Safeguarding LLMs Via Model-Agnostic Latent Safety Signals from Dark Knowledge

LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks including defenses at decoding stage that leverage models' hidden states. However, existing decoding-stage defenses suffer from two limitations. First,...

Adaptive Model Inversion Attacks Generalize a Privacy-Robustness Tradeoff

In this paper, we show that standard evaluations of high-resolution Model Inversion Attacks MIAs significantly underestimate training-data privacy leakage. State-of-the-art privacy defenses, standard training techniques such as MixUp and Adversarial Training, and undefended models all leak traini...

CVE-2026-97071: Incorrect Calculation

Incorrect Calculation vulnerability in VillaTheme CURCY woo-multi-currency allows Integer Attacks.This issue affects CURCY: from n/a through 2.2.17...

Online AutoML: Evaluating Poisoning Attacks on Adversarial Training Defense Strategy in IoT Networks

Machine learning ML-powered poisoning attack vectors are adversarial maneuvers whereby an attacker intentionally inserts, corrupts, or alters training data to distort an ML model's learning process. The objective is to diminish model efficacy, instill biases, induce misclassifications, or include...

Cross-Lingual Transferability of Training Data Extraction Attacks to Recover Memorized PII

The robustness of Personally Identifiable Information PII protection in Large Language Models LLMs is a critical concern, yet the risks associated with cross-lingual data extraction remain under-explored. This study evaluates the vulnerability of English-centric and multilingual models to Trainin...

edrEvasionWorkshop

edrEvasionWorkshop 「ステップバイステップのマルウェア開発：ローダーからカーネルまでEDRを回避する」 に関するワークショップ資料。DEF CON 34 および BSidesLV 2026 で発表されました。 このワークショップでは、カスタムマルウェア開発、EDRアーキテクチャと回避、C2のカスタマイズ、およびターゲットEDRとして Elastic Defend を使用したカーネルレベルの技術について探求します。 ステップバイステップのマルウェア開発：ローダーからカーネルまでEDRを回避する DEF CON 34 および BSidesLV 2026...

CLSIDFinder

CLSIDFinder Invoke-CLSIDFinder enumerates CLSID and AppID entries from the Windows registry to identify COM objects associated with Windows services via the LocalService value. The function correlates these entries, exports the results to a CSV file, and attempts COM activation when the related...

sshamble

sshamble SSHambleは、SSH実装を対象としたリサーチツールであり、以下を含みます： 認証に対する興味深い攻撃 セッション後の認証攻撃 認証前の状態遷移 認証タイミング分析 セッション後の列挙 このプロジェクトは作業中であり、急速に変更される可能性があります。 私たちのチームには researchα𝓽runZero.com で連絡できます。 https://SSHamble.com/ インストール バイナリはリリースページから入手できます。 注：MacOSでは、このバイナリに対してGatekeeperを無効にする必要がある場合があります。sudo xattr -rd...

OSCP Notes and Custom Dashboard

OSCP Field Notebook Tags: ligolo hashcat Port-indexed pentest methodology notebook: decision trees, copy-paste command patterns, and lessons from authorized practice boxes HTB / PG / personal labs. Merged from the older finalnotes-vault into this single tree. A workflow you can run under time...

Reflections and Fragments: Securing LLMs against Sequential Mosaic Attacks

Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversaries increasingly use mosaic attacks: multi-turn sequences whose individual fragments are innocuous in isolation yet assemble into a harmful...

Spectre-PoC

Spectre-PoC 내가 만든 것이 아님. 책에서 수집함. Spectre 공격: 추측 실행 악용...

Windows-RCE-exploits

ファイルレス攻撃に対するベンダーテスト用の私の他のGitHubリポジトリ - https://github.com/smgorelik/VendorSimTestScripts Windows-RCE-exploits エクスプロイトサンプルデータベースは、WINDOWS 向けのRCE （リモートコード実行）エクスプロイトおよび概念実証（Proof-of-Concepts）のリポジトリです。サンプルは、レッドチームおよびブルーチームの教育目的でアップロードされています。...

My-Presentation-Slides

내 프레젠테이션 슬라이드 Orange Tsai의 공개 발표 슬라이드입니다. 다음 곳에서 저를 찾을 수 있습니다: 블로그: https://blog.orange.tw/ 메일: [email protected] 트위터: @orange8361 2024 WorstFit: Windows ANSI에 숨겨진 변환기 공개! Black Hat Europe DEVCORE CONF 2025 Confusion Attacks: Apache HTTP Server에 숨겨진 의미론적 모호성 악용! Black Hat USA 추가 자료: 블로그 -...

poseidon

██████╗ ██████╗ ███████╗███████╗██╗██████╗ ██████╗ ███╗ ██╗ ██╔══██╗██╔═══██╗██╔════╝██╔════╝██║██╔══██╗██╔═══██╗████╗ ██║ ██████╔╝██║ ██║███████╗█████╗ ██║██║ ██║██║ ██║██╔██╗ ██║ ██╔═══╝ ██║ ██║╚════██║██╔══╝ ██║██║ ██║██║ ██║██║╚██╗██║ ██║ ╚██████╔╝███████║███████╗██║██████╔╝╚██████╔╝██║ ╚████...