SecML

SecML: セキュアで説明可能な機械学習のためのライブラリ SecMLは、機械学習（ML）アルゴリズムのセキュリティ評価 のためのオープンソースPythonライブラリです。 強力な機能を備えています： 幅広いMLアルゴリズムのサポート。 scikit-learnでサポートされているすべての教師あり学習アルゴリズムと、PyTorch深層学習プラットフォームによるニューラルネットワーク（NN）が利用可能です。 組み込みの攻撃アルゴリズム。 カスタム開発された高速ソルバーに基づく回避攻撃およびポイズニング攻撃。さらに、他のサードパーティの敵対的機械学習ライブラリへのコネクタも提供します。...

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...

Assessing Attack Surfaces in Generative Search Engines through Publisher Attributes: A Case Study in Political Domains

We characterize the attack surface of generative search engines GSEs against poisoning attacks in the political domain, from the perspectives of citation selection and personalization. GSEs integrate web search and answer generation with user preferences and backgrounds using large language model...

Empirical Analysis of Evasion and Poisoning against Malware Data Drift Detection

As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem. These data drift detector tools are designed for a different purpose and built with different techniques compared to...

SoK: Federated Learning for Intrusion Detection in Vehicular Networks

Modern vehicular networks face an expanding attack surface across internal Electronic Control Units ECUs and external Vehicle-to-Everything V2X communication. Federated Learning FL has emerged as a decentralized paradigm to deploy Intrusion Detection Systems IDS without compromising data privacy...

AoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics

Federated learning FL is attractive for cloud-edge intrusion detection because it enables collaborative training over distributed telemetry without centralizing raw logs. In production security analytics pipelines, however, only a subset of clients participates in each round, and heterogeneous...

Stealthy Poisoning Attacks Bypass Defenses in Regression Settings

Regression models are widely used in industrial processes, engineering and in natural and physical sciences, yet their robustness to poisoning has received less attention. When it has, studies often assume unrealistic threat models and are thus less useful in practice. In this paper, we propose a...

CVE-2022-33989

dproxy-nexgen aka dproxy nexgen uses a static UDP source port selected randomly only at boot time in upstream queries sent to DNS resolvers. This allows DNS cache poisoning because there is not enough entropy to prevent traffic injection attacks...

EUVD-2008-5207

Malware in sbrugna...

EUVD-2013-1174

Malware in sbrugna...

EUVD-2024-46429

Malicious code in bioql PyPI...

Decoding Deception: Understanding Automatic Speech Recognition Vulnerabilities in Evasion and Poisoning Attacks

Recent studies have demonstrated the vulnerability of Automatic Speech Recognition systems to adversarial examples, which can deceive these systems into misinterpreting input speech commands. While previous research has primarily focused on white-box attacks with constrained optimizations, and...

A Systematic Evaluation of Parameter-Efficient Fine-Tuning Methods for the Security of Code LLMs

Code-generating Large Language Models LLMs significantly accelerate software development. However, their frequent generation of insecure code presents serious risks. We present a comprehensive evaluation of seven parameter-efficient fine-tuning PEFT techniques, demonstrating substantial gains in...

On the Security and Privacy of Federated Learning: a Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

Federated Learning FL is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provide...

The Hidden Threat in Plain Text: Attacking RAG Data Loaders

Large Language Models LLMs have transformed human-machine interaction since ChatGPT's 2022 debut, with Retrieval-Augmented Generation RAG emerging as a key framework that enhances LLM outputs by integrating external knowledge. However, RAG's reliance on ingesting external documents introduces new...

Generalization under Byzantine and Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning

Whitepaper called Generalization Under Byzantine and Poisoning Attacks: Tight Stability Bounds In Robust Distributed Learning...

Hacklink Market Linked to SEO Poisoning Attacks in Google Results

Cybersecurity researchers at Netcraft have discovered a series of new SEO poisoning related attacks exploiting Google’s search results…...

Joint-GCG: Unified Gradient-Based Poisoning Attacks on Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation RAG systems enhance Large Language Models LLMs by retrieving relevant documents from external corpora before generating responses. This approach significantly expands LLM capabilities by leveraging vast, up-to-date external knowledge. However, this reliance on...

Benchmarking Poisoning Attacks against Retrieval-Augmented Generation

Retrieval-Augmented Generation RAG has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference. However, this integration introduces new security vulnerabilities, particularly to poisoning attacks. Although prior work has explore...

Securing Generative AI: Navigating Risk and Building Resilience

Running short on time but still want to stay in the know? Well, we’ve got you covered! We’ve condensed all the key takeaways into a handy audio summary. Our AI-driven podcasts are fit for on the go. Click right here to hear it all on CAASM & CDMB Inefficiencies! Generative AI has changed the way ...