Academic papers, new techniques, benchmarks, and theoretical findings in AI/LLM security.
Researchers created SOOM, a defense method that obfuscates (hides or disguises) deep learning operators to protect against model extraction attacks, where attackers reverse-engineer compiled neural network code to recreate trainable models. Built on TVM (a deep learning compiler), SOOM uses a machine learning cost model to scramble how operators work while keeping inference fast, achieving a 89% failure rate against extraction attacks with minimal performance slowdown.
Fix: The source proposes SOOM itself as the mitigation: a schedule-search-based operator obfuscation method built on TVM that constructs an obfuscation space for deep learning operators and uses a security-aware learned cost model based on XGBoost gradient boosted trees to generate obfuscated executable code for various deep learning operators, balancing security objectives with performance requirements.
IEEE Xplore (Security & AI Journals)MEC-Dedup is a security approach for mobile users storing data in cloud systems that use edge computing (processing done on devices near the user rather than in distant data centers). The system addresses risks that arise when multiple users' identical files are deduplicated (combined into one copy to save space), which could let attackers identify sensitive information. The research proposes methods to keep user data secure while still allowing the efficiency gains of deduplication in edge-assisted cloud storage.
This academic paper presents a new cryptographic method for secure communication between vehicles and infrastructure in VANETs (vehicular ad hoc networks, which are temporary networks formed by moving vehicles). The scheme uses identity-based aggregate signcryption (a technique that combines digital signatures for authentication with encryption for confidentiality, while processing multiple messages together), and the authors claim to have mathematically proven it cannot be broken by attackers.
This academic paper presents a new method for detecting unusual network activity using parallel GANs (generative adversarial networks, AI systems that learn patterns by comparing real data against artificially generated data) without requiring manually set detection thresholds (cutoff points that decide what counts as suspicious). The approach uses comparative reconstruction error learning, meaning it compares how well the AI can recreate normal network behavior to spot deviations that might indicate attacks or intrusions.
This research article examines whether training employees on cybersecurity improves their understanding of password security and safe Wi-Fi practices. The study, published in June 2026, investigates the connection between formal security education and employees' actual knowledge of protective measures in their daily work.
Researchers developed a new membership inference attack (MIA, a method to determine whether specific data was used to train an AI model) called MU-MIA that uses machine unlearning (a technique to make a model forget specific training samples) to track how a model forgets information about individual samples. The attack works by monitoring changes in the model's behavior as it unlearns each sample and uses a BiLSTM classifier (a type of neural network that analyzes sequences of data) to distinguish between samples that were in the training data versus those that weren't.
This research paper analyzes how networked control systems (computer systems where multiple sensors share information across a network) behave when under attack from false data injection, or FDI (inserting fake sensor readings to disrupt the system). Using game theory (a mathematical framework for analyzing competing strategies), the researchers model the conflict between legitimate system operators trying to keep data accurate and attackers trying to corrupt it, then prove that both sides will reach a stable strategic equilibrium (a predictable outcome where neither side can improve by changing tactics alone).
A study in Sierra Leone tested whether AI (specifically Google's Gemini) could help students learn math better by acting as a teaching partner rather than replacing teachers. The AI was designed using a 'Socratic' approach, asking guiding questions instead of giving direct answers, and students who used it showed significant learning gains equivalent to 1.2 to 2.5 years of typical progress in just eight weeks, while maintaining high engagement and shifting their own questions toward understanding rather than just seeking solutions.
This academic paper describes a new encryption method designed to let multiple people search through encrypted medical data while protecting privacy and controlling who has access. The scheme uses lattice-based cryptography (a type of math-hard encryption based on complex grid structures) and allows for selective disclosure (sharing only certain information with specific people) and user revocation (removing someone's access rights). This addresses the challenge of keeping medical information secure while still making it searchable and shareable in healthcare systems.
This academic paper describes a method for securely sharing secret images among multiple people using the Chinese Remainder Theorem (a mathematical technique for solving certain types of equations) and polynomials (mathematical expressions with variables). The scheme includes a certification process to verify that the shared image pieces are authentic and haven't been tampered with.
This academic paper proposes TMAS, a threshold multi-auditor auditing scheme designed to verify data integrity and security in cloud-fog computing environments (distributed systems where data processing happens both in the cloud and at edge devices closer to users) where trust between parties is limited. The scheme uses multiple independent auditors working together so that no single auditor needs to be completely trusted, addressing the challenge of maintaining security when collaborating systems don't fully trust each other.
STAFF is a research tool for testing firmware (the low-level software that runs on hardware devices) by using fuzzing (automated testing that feeds random or specially crafted inputs to find bugs). The tool uses stateful taint analysis (tracking how untrusted data flows through a program) to improve the fuzzing process and find security vulnerabilities more effectively in full systems.
Researchers have developed PLC-Defuser, a tool that detects hidden malicious code (logic bombs, which are programmed instructions designed to execute harmful actions when triggered) in PLCs (programmable logic controllers, computers used to automate industrial equipment like factory machinery). The tool uses control flow graphs (visual maps showing how a program's instructions connect and execute) and model checking (automated verification that tests whether software meets safety properties) to find these threats before they can cause damage.
This research paper describes a method for creating attack graphs (visual maps showing how attackers could move through a company's computer network) by using data collected from endpoint devices (individual computers and servers). The approach is designed to scale efficiently, meaning it can handle large enterprise networks without becoming too slow or resource-intensive. The work was published in October 2026 in the journal Computers & Security.
CoolTest is a new randomness test designed to work well with small amounts of data, published in November 2026. Randomness tests check whether data appears truly random or follows a pattern, which is important for security applications like cryptography (the practice of encoding information to keep it secret). This tool addresses a limitation of existing tests that often require large datasets to work accurately.
This research paper from November 2026 examines threat models for industrial control systems (ICS, the computers that manage factories, power plants, and other critical infrastructure) by developing systematic methods to search for and identify security threats. The authors appear to connect threat identification approaches with pentesting (penetration testing, where security experts deliberately try to break into systems to find weaknesses). The paper contributes to understanding how to better protect critical infrastructure from cyberattacks.
This academic paper presents ODHD, a method for generating helper data that enables reliable key derivation from SRAM PUF (static random-access memory physical unclonable function, a hardware feature that extracts unique cryptographic keys from the inherent variations in memory chips) without needing non-volatile memory (permanent storage like flash drives). The approach addresses the challenge of creating stable, reproducible keys from noisy hardware sources for secure cryptographic applications in resource-constrained devices.
This academic review examines intrusion detection systems (IDS, software that monitors networks to catch unauthorized access attempts) designed specifically for Internet of Medical Things (IoT devices like connected medical equipment that collect and share health data). The paper analyzes these systems across four key areas: how well they catch attacks, how efficiently they run, whether humans can understand why they flag something as a threat, and whether they work reliably on new types of attacks they haven't seen before.
Researchers discovered a new type of backdoor attack (hidden malicious code inserted into AI systems) that works against personalized federated learning (a privacy-focused method where multiple computers train an AI model together without sharing raw data). The attack is designed to be stealthy and persistent, meaning it can hide from detection and remain in the system over time.
This research paper presents a framework for understanding how reinforcement learning-based cyber agents (AI systems trained to make decisions by trial and error in cybersecurity contexts) make their decisions. The authors developed a multi-layer approach to explain the "black box" problem (the difficulty in understanding why AI systems reach certain conclusions), which is important for security experts to verify that these AI agents are operating correctly and safely.