CVE-2019-15487

DfE School Experience before v16333-GA has XSS via a teacher training URL...

INE Security Partners with Abadnet Institute for Cybersecurity Training Programs in Saudi Arabia

Cary, North Carolina, 22nd May 2025, CyberNewsWire...

ReCopilot: Reverse Engineering Copilot in Binary Analysis

Binary analysis plays a pivotal role in security domains such as malware detection and vulnerability discovery, yet it remains labor-intensive and heavily reliant on expert knowledge. General-purpose large language models LLMs perform well in programming analysis on source code, while...

Covert Attacks on Machine Learning Training in Passively Secure MPC

Secure multiparty computation MPC allows data owners to train machine learning models on combined data while keeping the underlying training data private. The MPC threat model either considers an adversary who passively corrupts some parties without affecting their overall behavior, or an adversa...

Training-Free Watermarking for Autoregressive Image Generation

Invisible image watermarking can protect image ownership and prevent malicious misuse of visual generative models. However, existing generative watermarking methods are mainly designed for diffusion models while watermarking for autoregressive image generation models remains largely underexplored...

An Alignment between the CRA'S Essential Requirements and the ATT&CK'S Mitigations

The paper presents an alignment evaluation between the mitigations present in the MITRE's ATT&CK framework and the essential cyber security requirements of the recently introduced Cyber Resilience Act CRA in the European Union. In overall, the two align well with each other. With respect to the...

Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?

Low rank adaptation LoRA has emerged as a prominent technique for fine-tuning large language models LLMs thanks to its superb efficiency gains over previous methods. While extensive studies have examined the performance and structural properties of LoRA, its behavior upon training-time attacks...

PoLO: Proof-Of-Learning and Proof-Of-Ownership at Once with Chained Watermarking

Machine learning models are increasingly shared and outsourced, raising requirements of verifying training effort Proof-of-Learning, PoL to ensure claimed performance and establishing ownership Proof-of-Ownership, PoO for transactions. When models are trained by untrusted parties, PoL and PoO mus...

R1dacted: Investigating Local Censorship in DeepSeek'S R1 Language Model

DeepSeek recently released R1, a high-performing large language model LLM optimized for reasoning tasks. Despite its efficient training pipeline, R1 achieves competitive performance, even surpassing leading reasoning models like OpenAI's o1 on several benchmarks. However, emerging reports suggest...

Trend Joins NVIDIA to Secure AI Infrastructure with NVIDIA

Together, we are focused on securing the full AI lifecycle—from development and training to deployment and inference—across cloud, data center, and AI factories...

Self-Destructive Language Model

Harmful fine-tuning attacks pose a major threat to the security of large language models LLMs, allowing adversaries to compromise safety guardrails with minimal harmful data. While existing defenses attempt to reinforce LLM alignment, they fail to address models' inherent "trainability" on harmfu...

Facial Recognition Leveraging Generative Adversarial Networks

Face recognition performance based on deep learning heavily relies on large-scale training data, which is often difficult to acquire in practical applications. To address this challenge, this paper proposes a GAN-based data augmentation method with three key contributions: 1 a residual-embedded...

Meta sent cease and desist letter over AI training

EU privacy advocacy group NOYB has clapped back at Meta over its plans to start training its AI model on European users' data. In a cease and desist letter to the social networking giant's Irish operation signed by founder Max Schrems, the non-profit demanded that it justify its actions or risk...

GenoArmory: a Unified Evaluation Framework for Adversarial Attacks on Genomic Foundation Models

We propose the first unified adversarial attack benchmark for Genomic Foundation Models GFMs, named GenoArmory. Unlike existing GFM benchmarks, GenoArmory offers the first comprehensive evaluation framework to systematically assess the vulnerability of GFMs to adversarial attacks. Methodologicall...

Noyb Threatens Meta with Lawsuit for Violating GDPR to Train AI on E.U. User Data From May 27

Austrian privacy non-profit noyb none of your business has sent Meta's Irish headquarters a cease-and-desist letter, threatening the company with a class action lawsuit if it proceeds with its plans to train users' data for training its artificial intelligence AI models without an explicit opt-in...

Securing the Code: Building a Culture of Credential Protection in Dev Teams

Credential protection is key to preventing breaches. Secure APIs, rotate secrets and train devs to handle credentials safely…...

On Technique Identification and Threat-Actor Attribution Using LLMs and Embedding Models

Attribution of cyber-attacks remains a complex but critical challenge for cyber defenders. Currently, manual extraction of behavioral indicators from dense forensic documentation causes significant attribution delays, especially following major incidents at the international scale. This research...

AC-LoRA: (Almost) Training-Free Access Control-Aware Multi-Modal LLMs

Corporate LLMs are gaining traction for efficient knowledge dissemination and management within organizations. However, as current LLMs are vulnerable to leaking sensitive information, it has proven difficult to apply them in settings where strict access control is necessary. To this end, we desi...

Learning How to Hack: Why Offensive Security Training Benefits Your Entire Security Team

Organizations across industries are experiencing significant escalations in cyberattacks, particularly targeting critical infrastructure providers and cloud-based enterprises. Verizon's recently released 2025 Data Breach Investigations Report found an 18% YoY increase in confirmed breaches, with...

Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data

Federated learning FL presents an effective solution for collaborative model training while maintaining data privacy across decentralized client datasets. However, data quality issues such as noisy labels, missing classes, and imbalanced distributions significantly challenge its effectiveness. Th...