arxiv2d ago

Unveiling Unicode's Unseen Underpinnings in Undermining Authorship Attribution

arXiv:2508.15840v5 Announce Type: replace-cross Abstract: When using a public communication channel--whether formal or informal, such as commenting or posting on social media--end users have no expectation of privacy: they compose a message and broadcast it for the world to see. Even if an end user

#security #steganography #stylometry Read on arxiv →

arxiv4d ago

Owner-Harm: A Missing Threat Model for AI Agent Safety

arXiv:2604.18658v1 Announce Type: cross Abstract: Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a distinct and commercially consequential threat category: agents harming their own deployers. Real-wor

AGAGLL3 models #safety #security #benchmark Read on arxiv →

arxiv4d ago

Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection

arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious payload while preserving its behavior. These transformations make it difficult for traditional and m

LA1 model #security #obfuscation #machine-learning Read on arxiv →

arxiv5d agobearish

The Blind Spot of Agent Safety: How Benign User Instructions Expose Critical Vulnerabilities in Computer-Use Agents

arXiv:2604.10577v2 Announce Type: replace-cross Abstract: Computer-use agents (CUAs) can now autonomously complete complex tasks in real digital environments, but when misled, they can also be used to automate harmful actions programmatically. Existing safety evaluations largely target explicit thre

CL1 model #safety #security #benchmark Read on arxiv →

arxivApr 17

Between a Rock and a Hard Place: The Tension Between Ethical Reasoning and Safety Alignment in LLMs

arXiv:2509.05367v4 Announce Type: replace-cross Abstract: Large Language Model safety alignment predominantly operates on a binary assumption that requests are either safe or unsafe. This classification proves insufficient when models encounter ethical dilemmas, where the capacity to reason through

#safety #security #cryptography Read on arxiv →

arxivApr 16

Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference

arXiv:2604.12168v1 Announce Type: cross Abstract: The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transportation, and information security, have led to significant improvements in service efficiency and low

DE1 model #security #cryptography #homomorphic-encryption Read on arxiv →

arxivApr 11

Are GUI Agents Focused Enough? Automated Distraction via Semantic-level UI Element Injection

arXiv:2604.07831v1 Announce Type: cross Abstract: Existing red-teaming studies on GUI agents have important limitations. Adversarial perturbations typically require white-box access, which is unavailable for commercial systems, while prompt injection is increasingly mitigated by stronger safety alig

#security #adversarial #computer-vision Read on arxiv →

arxivApr 10bearish

CAAP: Capture-Aware Adversarial Patch Attacks on Palmprint Recognition Models

arXiv:2604.06987v1 Announce Type: cross Abstract: Palmprint recognition is deployed in security-critical applications, including access control and palm-based payment, due to its contactless acquisition and highly discriminative ridge-and-crease textures. However, the robustness of deep palmprint re

#security #adversarial-attacks #computer-vision Read on arxiv →

arxivApr 6

Learning the Signature of Memorization in Autoregressive Language Models

arXiv:2604.03199v1 Announce Type: cross Abstract: All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the designer's intuition. We introduce the first transferable learned attack,

MARWRE3 models #membership inference #language models #transfer learning Read on arxiv →

thevergeApr 2bearish

PSA: Anyone with a link can view your Granola notes by default

If you use the AI-powered note-taking app Granola, you might want to double-check your privacy settings. Though Granola says your notes are "private by default," it makes them viewable to anyone with a link, and also uses them for internal AI training unless you opt out. Granola describes itself as

GR1 model #privacy #security #ai-training Read on theverge →