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One-Time Passwords (OTPs, temporary codes used in two-factor authentication to verify your identity) like HOTP and TOTP have vulnerabilities that let attackers bypass security if they steal the secret key stored on a device or server. This paper proposes HP-OTP, a new OTP scheme that combines your password with the device's unique identifier to make it harder for attackers to forge codes even if they compromise either the device or server.

Federated learning (a system where decentralized devices train a shared AI model together while keeping their data local) is vulnerable to poisoning attacks, where malicious participants inject false data to corrupt the final model. This paper proposes AdaptiveShield, a defense system that uses dynamic detection strategies to identify attackers, automatically adjusts its sensitivity thresholds to handle different attack types, reduces damage from missed attackers by adjusting hyperparameters (settings that control how the model learns), and hides user identities through a shuffling mechanism to protect privacy.

Deep learning attacks have successfully cracked CAPTCHAs (automated tests that distinguish humans from bots) that use large character sets, especially those with alphabets from languages like Chinese. This paper proposes ACG (Adversarial Large Character Set CAPTCHA Generation), a framework that makes CAPTCHAs harder to attack by adding adversarial perturbations (intentional distortions that confuse AI recognition systems) through two modules: one that prevents character recognition and another that adds global visual noise, reducing attack success rates from 51.52% to 2.56%.

This research paper addresses generalized out-of-distribution detection (OOD detection, where an AI system identifies inputs that are very different from its training data), which is important for AI systems used in safety-critical applications. Rather than focusing on designing better scoring functions, the authors propose a new decision rule called the generalized Benjamini Hochberg procedure that uses hypothesis testing (a statistical method for making decisions about data) to determine whether an input is out-of-distribution, and they prove this method controls false positive rates better than traditional threshold-based approaches.

Researchers developed a new method for backdoor attacks (techniques that manipulate AI systems to behave in specific ways when exposed to hidden trigger patterns) that works better in real-world physical scenarios. The method, called VSSC triggers (Visible, Semantic, Sample-specific, and Compatible), uses large language models, generative models, and vision-language models in an automated pipeline to create stealthy triggers that can survive visual distortions and be deployed using real objects, making physical backdoor attacks more practical and systematic than manual methods.

This paper presents Test-Time Correction (TTC), a system that helps autonomous vehicles fix detection errors while driving, rather than waiting for retraining. TTC uses an Online Adapter module with visual prompts (image-based descriptions of objects derived from feedback like mismatches or user clicks) to continuously correct mistakes in real-time, allowing vehicles to adapt to new situations and improve safety without stopping to retrain the system.

This research proposes a new framework for side-channel analysis (SCA, a type of attack that exploits physical information like power consumption or timing to break cryptography) by combining multiple different leakage models (ways of measuring how a cryptographic device leaks secrets) using ensemble learning (combining many weaker models into one stronger one). The framework improves how well attackers can recover secret keys by using deep learning with complementary information from different measurement approaches, and the authors prove mathematically that their ensemble model gets closer to the true secret distribution.

Remote attestation (RA, the process of verifying that software running on a trusted computer processor is genuine and hasn't been tampered with) traditionally relies on a single central authority to verify trust, which creates security vulnerabilities. This paper introduces Janus, a new RA system that spreads trust across multiple parties using physical hardware features (PUF, or physically unclonable function, unique identifiers built into computer chips) and smart contracts (automated programs running on blockchain networks) to make the verification process more secure, flexible, and resistant to attacks.

Langflow versions up to 1.6.9 have a chained vulnerability that allows attackers to take over user accounts and run arbitrary code on the system. The flaw combines two misconfigurations: overly permissive CORS settings (CORS, or cross-origin resource sharing, allows webpages from different domains to access each other) that accept requests from any origin with credentials, and refresh token cookies (a token used to get new access credentials) set to SameSite=None, which allows a malicious webpage to steal valid tokens and impersonate a victim.

Graph Neural Networks (GNNs, machine learning models that work with interconnected data) perform poorly at detecting anomalies in graphs because of high Class Homophily Variance (CHV), meaning some node types cluster together while others scatter. The researchers propose HEAug, a new GNN model that creates additional connections between nodes that are similar in features but not originally linked, and adjusts its training process to avoid generating unwanted connections.

The AI industry is gradually accepting LLM (large language model) outputs as reliable without questioning them, similar to how NASA ignored warning signs before the Challenger disaster. This 'normalization of deviance' (accepting behavior that deviates from proper standards as normal) is particularly risky in agentic systems (AI systems that can take independent actions without human approval at each step), where unchecked LLM decisions could cause serious problems.