Research

This academic survey examines how well large language model-based agents (AI systems that use LLMs to make decisions and take actions) can generalize, meaning how effectively they perform on new tasks or situations they weren't specifically trained for. The paper reviews research across different domains to understand what factors help or limit an agent's ability to adapt and work reliably in unfamiliar contexts.

Quantum computing poses a major threat to current security systems because it can break traditional encryption methods that protect critical infrastructure and cloud services. This paper examines how quantum computing affects different layers of infrastructure (from applications to networks) and proposes moving toward quantum-resistant cryptography (encryption methods designed to withstand quantum computer attacks) as a protective strategy. The authors advocate for collaboration across sectors to develop and implement these new security approaches before quantum threats become critical.

This research paper proposes a method to detect FDI attacks (false data injection, where attackers insert fake sensor readings into control systems) by using encoding techniques to transform measurement data into a different mathematical space. The approach aims to catch stealthy FDI attacks that are designed to evade traditional detection methods by disguising themselves as normal system behavior.

This research paper, published in June 2026, explores how to make multi-party private set union (a process where multiple parties combine datasets while keeping their individual data secret) more efficient and secure against malicious attacks. The authors propose using trusted execution environments (TEEs, hardware that protects code and data even from the computer's owner) to achieve this goal. The paper aims to balance computational efficiency with strong security guarantees when multiple parties need to collaborate while protecting sensitive information.

Researchers used LLMs (large language models, AI systems trained on vast text data) and model checking (a technique to verify if software behaves correctly by examining all possible states) to study Android's permission system, which controls what apps can access on your phone. The study involved modeling how this system works, checking if it's secure, and finding ways to exploit it using AI techniques.

This research paper examines how visual explanation techniques can help protect federated learning (a machine learning approach where multiple computers train a model together without sharing raw data) from poisoning attacks (attempts to corrupt the training data or model). The authors propose an enhanced version of LayerCAM (a method that visualizes which parts of an input an AI focuses on), combined with autoencoders (neural networks that compress and reconstruct data), to detect and defend against such attacks.

Researchers developed new data augmentation strategies (techniques for artificially expanding training datasets) to improve website fingerprinting, which is a method to identify which websites users visit by analyzing their network traffic patterns. The study, published in August 2026, demonstrates how combining multiple augmentation approaches can make these fingerprinting techniques more effective.

This research paper describes a method called ReSLC that protects AI systems used to find software bugs from backdoor attacks, where attackers secretly embed malicious instructions into the AI's training process. The approach uses redundant semantic LLM compression (a technique that removes unnecessary information from large language models while keeping their core abilities) to make these hidden attacks harder to carry out. The work was published in July 2026 in the Journal of Information Security and Applications.

This research paper presents a hybrid deep learning method using autoencoders (neural networks that learn to compress and reconstruct data) and transformers (AI models that process sequences of information) to detect a new type of attack called unresponsive ECN attacks on low-latency network services (systems designed to minimize delay in data transmission). The proposed method achieves over 90% accuracy in detecting these attacks while keeping false alarms below 0.01%, outperforming existing detection approaches by more than 10%.

This paper presents XFaceMark, a method that uses YOLO (an object detection system that identifies items in images) and random MRFO (a nature-inspired optimization algorithm) to add watermarks to deepfakes (AI-generated fake videos or images) in a way that can be explained and understood. The approach aims to make deepfakes traceable while allowing researchers to understand how the watermarking process works.

This academic paper discusses extending SBOMs (software bill of materials, which are detailed lists of all components and dependencies in software) to create AIBOMs that can describe agentic AI systems (AI systems that can take independent actions and make decisions). The paper proposes schema extensions, methods for coordinating multiple AI agents, and ways to evaluate whether AI systems produce consistent and reproducible results.

This research paper describes a method for automatically generating password mangling rules (transformations that modify passwords systematically) using adaptive density clustering (a technique that groups similar data points together based on how densely packed they are). The approach aims to improve password security by learning patterns from real password data to create more effective rules for testing password strength.

This is a survey paper published in an academic journal that reviews recent progress in conversational data generation, which refers to techniques for creating dialogue datasets (collections of conversations) used to train and improve AI systems. The paper appears to be a comprehensive overview of advances in this field as of July 2026, but no specific technical findings, vulnerabilities, or security issues are described in the provided content.

Researchers have developed AISM (adversarial image steganography model, a technique that hides data inside images while making them resistant to AI recognition), a method for protecting images from being recognized by unauthorized AI systems. The approach uses adversarial techniques (methods that deliberately trick AI models by adding subtle, invisible changes to data) combined with steganography (the practice of hiding information within other data) to prevent unwanted AI analysis while keeping the images visually normal to humans. This work addresses privacy concerns where people want to prevent their images from being processed by AI systems without permission.

This is an erratum (correction notice) for an academic survey paper about adversarial machine learning in IoT security (the practice of deliberately fooling AI systems used to protect internet-connected devices). The notice appears in ACM Computing Surveys journal, Volume 58, Issue 10, published in July 2026.

This is an academic survey paper that reviews different prompting frameworks, which are structured approaches to asking large language models (AI systems trained on huge amounts of text) questions or giving them instructions to complete tasks. The paper, published in a major computer science journal, catalogues and analyzes various methods researchers have developed to improve how effectively people interact with and get useful results from LLMs.

Version 5.5.0 adds new security techniques documenting threats to AI systems, including AI agent tool poisoning (when attackers corrupt tools that AI agents use), supply chain attacks, and cost harvesting (depleting computing resources through expensive queries). It also updates existing techniques and mitigations related to code signing and monitoring AI agent behavior.

RanDS is a new large-scale dataset containing raw binary files (the compiled machine code of programs) and extracted features designed to help researchers study and detect ransomware (malicious software that encrypts victims' files and demands payment). This resource aims to support the development and testing of machine learning models that can identify ransomware threats more effectively.

Researchers discovered a new backdoor attack (a security flaw where hidden malicious code is planted in training data) on Graph Neural Networks, or GNNs (AI models designed to understand interconnected data). The attack uses a single trigger node (a specially crafted fake data point) attached to a target node to trick the GNN into making wrong predictions not just on that node, but also on its immediate neighbors, while remaining stealthy and achieving over 95% success rates even against existing defenses.