AFL

american fuzzy lop 最初由 Michal Zalewski [email protected] 开发。 如果没有时间阅读本文件，请参阅 QuickStartGuide.txt。 1 引导式模糊测试的挑战 模糊测试是识别真实世界软件安全问题的最强大且久经考验的策略之一；迄今为止，在安全关键软件中发现的大多数远程代码执行和权限提升漏洞都要归功于它。 遗憾的是，模糊测试也相对浅显；盲目的随机变异使得测试代码中的某些代码路径很难被触及，导致一些漏洞完全超出这种技术的可达范围。 为了解决这个问题，人们进行了大量尝试。早期方法之一——由 Tavis Ormandy...

shfz

shfz 遺伝的アルゴリズムによるファズ生成をサポートする、シナリオベースのWebアプリケーションファジングツールです。 特徴 簡単にカスタマイズ可能 : JavaScript / TypeScript でファジングシナリオをスクリプト化 サードパーティパッケージ をファジングシナリオで利用可能 遺伝的アルゴリズム によるファズ生成でコードカバレッジ向上 CIとの親和性が高く 、自動でGitHub Issueにレポートを投稿 トレーサーの自動エラー検出 でバグ修正を支援 インストール Releases ページからバイナリをダウンロードするか、ソースからコンパイルしてください。 Linux...

frelatage

pip3 install frelatage 世界が待ち望んだPythonファザー インストール | 仕組み | 機能 | Frelatageの使い方 | 設定 Frelatageは、カバレッジベースのPythonファジングライブラリで、Pythonコードをファジングするために使用できます。Frelatageの開発は、AFL/AFL++、Atheris、PythonFuzzなど、さまざまなファザーから着想を得ています。このプロジェクトの主な目的は、これらのファザーの優れた機能を活用し、それらを一つの新しいツールにまとめて、Pythonアプリケーションを効率的にファジングすることです。...

RAMP: Reversing Adversarial Perturbations to Strengthen Clean-Label Backdoor Attacks against Malware Detectors

Deep learning-based malware detectors are commonly updated by fine-tuning on newly collected samples, but this practical update pipeline also creates an attack surface for training-time backdoor attacks. In realistic crowdsourced data collection, however, strict label vetting typically restricts...

American Fuzzy Lop plus plus 5.03c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

Survival Of~The~Stealthiest: Evolving Low-Entropy Ransomware Via~Genetic Algorithms

Traditional ransomware deployment often relies on massive encryption procedure, triggering immediate detection by modern defense systems. This work introduces a paradigm shift in cryptographic attacks by framing ransomware execution as a Search-Based Software Engineering SBSE optimization problem...

A Multi-Objective AutoML-Based Efficient Intrusion Detection System for EV Charging Networks

Electric Vehicle Charging Systems EVCSs are increasingly connected with Internet of Things IoT devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems IDSs are essential for securing EV charging networks; however, conventional...

American Fuzzy Lop plus plus 5.01c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

American Fuzzy Lop plus plus 5.00c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

American Fuzzy Lop plus plus 4.40c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

American Fuzzy Lop plus plus 4.35c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

Optimizing Epsilon Security Parameters in QKD

We investigate the optimization of epsilon-security parameters in quantum key distribution QKD, aiming to improve the achievable secure key rate under a fixed overall composable security level. For this purpose, we employ a continuous genetic algorithm CGA to optimize the epsilon-security...

Black-Box Guardrail Reverse-Engineering Attack

Large language models LLMs increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful responses, these guardrails introduce a new class of vulnerabilities by exposing observable decision patterns. In this...

Genesis: Evolving Attack Strategies for LLM Web Agent Red-Teaming

As large language model LLM agents increasingly automate complex web tasks, they boost productivity while simultaneously introducing new security risks. However, relevant studies on web agent attacks remain limited. Existing red-teaming approaches mainly rely on manually crafted attack strategies...

American Fuzzy Lop plus plus 4.34c

Google's American Fuzzy Lop is a brute-force fuzzer coupled with an exceedingly simple but rock-solid instrumentation-guided genetic algorithm. afl++ is a superior fork to Google's afl. It has more speed, more and better mutations, more and better instrumentation, custom module support, etc...

ENJ: Optimizing Noise with Genetic Algorithms to Jailbreak LSMs

The widespread application of Large Speech Models LSMs has made their security risks increasingly prominent. Traditional speech adversarial attack methods face challenges in balancing effectiveness and stealth. This paper proposes Evolutionary Noise Jailbreak ENJ, which utilizes a genetic algorit...

A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things

The rapid expansion of the Internet of Things IoT and Wireless Sensor Networks WSNs has significantly increased the attack surface of such systems, making them vulnerable to a wide range of cyber threats. Traditional Intrusion Detection Systems IDS often fail to meet the stringent requirements of...

Enhancing Software Vulnerability Detection through Adaptive Test Input Generation Using Genetic Algorithm

Software vulnerabilities continue to undermine the reliability and security of modern systems, particularly as software complexity outpaces the capabilities of traditional detection methods. This study introduces a genetic algorithm-based method for test input generation that innovatively...

Enhancing Jailbreak Attacks on LLMs Via Persona Prompts

Jailbreak attacks aim to exploit large language models LLMs by inducing them to generate harmful content, thereby revealing their vulnerabilities. Understanding and addressing these attacks is crucial for advancing the field of LLM safety. Previous jailbreak approaches have mainly focused on dire...

Stealix: Model Stealing Via Prompt Evolution

Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data...