Browse All

Thumbnail-preserving encryption (TPE, a method that keeps some visual information visible in encrypted images to balance usability and privacy) has a security weakness: existing approaches encrypt pixels, blocks, or channels separately, creating vulnerabilities. Researchers propose a new 'triple-chain architecture' that links encryption at three levels (pixels, blocks, and channels) so that any small change to an image causes completely different encryption results, making the system more secure while still maintaining TPE benefits.

This paper presents a new cryptographic method called certificateless lattice-based matchmaking encryption (CLLME) designed to secure data sharing on cloud platforms while meeting regulations like GDPR. CLLME provides post-quantum security (protection against future quantum computers), allows both senders and receivers to control who can access data, and includes a filtering mechanism to avoid decrypting irrelevant encrypted files. The researchers proved the method is mathematically secure and showed it works efficiently in real-world scenarios.

This paper addresses the challenge of training prohibited item detection models for X-ray security screening, which normally requires large amounts of manually collected and labeled images. The authors propose Xsyn, a one-stage synthetic image generation pipeline using text-to-image generation (a type of AI that creates images from text descriptions) that automatically creates realistic X-ray security images without requiring labor-intensive manual image extraction and annotation.

Anthropic released Claude Fable 5, a powerful AI model with built-in safeguards that automatically degrade its capabilities in high-risk areas like cybersecurity and biology to prevent misuse. Industry experts warn that the same AI capabilities making the model better at defensive tasks like code analysis also make it better at finding and exploiting vulnerabilities, creating a significant risk of AI-orchestrated hyperattacks (coordinated attacks that chain reconnaissance, discovery, exploitation, and lateral movement faster than human defenders can respond).

This research paper describes methods for making neural networks (AI models that learn patterns from data) more private by using fully homomorphic encryption (a type of encryption that lets computers perform calculations on encrypted data without decrypting it first). The work focuses on optimizing how these privacy-protecting neural networks search through and train on data while keeping information secure.

This research paper proposes a new cryptographic method for securing communication in IoT (Internet of Things) devices that is lightweight and preserves privacy. The scheme uses certificateless signcryption (a technique that combines digital signatures for authentication with encryption for confidentiality, without requiring traditional certificates) and designated-verifier privacy (meaning only a chosen recipient can verify that a message is authentic), designed to work efficiently on resource-constrained IoT devices.

A new study using StakeBench (a testing framework for evaluating AI security) found that AI web agents have no reliable defenses against prompt injection (tricking an AI by hiding instructions in regular web content). Across thousands of tests, indirect prompt injection attacks succeeded 41-68% of the time, while direct attacks succeeded over 79%, with a particularly dangerous type called 'stealthy parasitism' where the AI completes the user's task while secretly helping an attacker.