CVE-2026-41338 OpenClaw < 2026.3.31 - Time-of-Check-Time-of-Use (TOCTOU) Vulnerability in Sandbox File Operations

OpenClaw before 2026.3.31 contains a time-of-check-time-of-use vulnerability in sandbox file operations that allows attackers to bypass fd-based defenses. Attackers can exploit check-then-act patterns in applypatch, remove, and mkdir operations to manipulate files between validation and execution...

Containing a domain compromise: How predictive shielding shut down lateral movement

In this article 1. Predictive shielding overview 2. Attack chain overview 3. How predictive shielding changed the outcome 4. MITRE ATT&CK® techniques observed 5. Learn more In identity-based attack campaigns, any initial access activity can turn an already serious intrusion into a critical incide...

Primer on GitHub Actions Security - Threat Model, Attacks and Defenses (Part 1/2)

Understanding and defending your GitHub Actions - from threat model to security controls...

What Is Threat Hunting? A Complete Guide for Security Teams

What Is Threat Hunting? A Complete Guide for Security Teams Security tools catch a lot. They do not catch everything. Automated detection systems rely on known signatures, predefined rules, and behavioral baselines. Sophisticated adversaries know this and design their operations to slip through t...

Follow My Eyes: Backdoor Attacks on VLM-Based Scanpath Prediction

Scanpath prediction models forecast the sequence and timing of human fixations during visual search, driving foveated rendering and attention-based interaction in mobile systems where their integrity is a first-class security concern. We present the first study of backdoor attacks against VLM-bas...

Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions

Retrieval-augmented generation RAG significantly enhances large language models LLMs but introduces novel security risks through external knowledge access. While existing studies cover various RAG vulnerabilities, they often conflate inherent LLM risks with those specifically introduced by RAG. I...

AttackEval: A Systematic Empirical Study of Prompt Injection Attack Effectiveness against Large Language Models

Prompt injection has emerged as a critical vulnerability in large language model LLM deployments, yet existing research is heavily weighted toward defenses. The attack side -- specifically, which injection strategies are most effective and why -- remains insufficiently studied.We address this gap...

Architecting Secure AI Agents: Perspectives on System-Level Defenses against Indirect Prompt Injection Attacks

AI agents, predominantly powered by large language models LLMs, are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions. This position paper discusses our vision for system-level defenses against indirect prompt...

Masters of Imitation: How Hackers and Art Forgers Perfect the Art of Deception

Unmasking impostors is something the art world has faced for decades, and there are valuable lessons from the works of Elmyr de Hory that can apply to the world of defensive cybersecurity. During the 1960s, de Hory gained infamy as a premier forger, passing off counterfeit masterworks of Picasso,...

A Framework for Formalizing LLM Agent Security

Security in LLM agents is inherently contextual. For example, the same action taken by an agent may represent legitimate behavior or a security violation depending on whose instruction led to the action, what objective is being pursued, and whether the action serves that objective. However,...

PISmith: Reinforcement Learning-Based Red Teaming for Prompt Injection Defenses

Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against adaptive attacks remains insufficiently evaluated, potentially creating a false sense of security. In this work, we...

Security Considerations for Artificial Intelligence Agents

This article, a lightly adapted version of Perplexity's response to NIST/CAISI Request for Information 2025-0035, details our observations and recommendations concerning the security of frontier AI agents. These insights are informed by Perplexity's experience operating general-purpose agentic...

Cybersecurity AI: Hacking Consumer Robots in the AI Era

Is robot cybersecurity broken by AI? Consumer robots -- from autonomous lawnmowers to powered exoskeletons and window cleaners -- are rapidly entering homes and workplaces, yet their security remains rooted in assumptions of specialized attacker expertise. This paper presents evidence that...

How Each Gulf Country Is Intercepting Iranian Missiles and Drones

As missiles and drones cross the region’s skies, the Gulf’s layered air-defense networks—from THAAD to Patriot batteries—are being tested in real time...

On the Practical Feasibility of Harvest-Now, Decrypt-Later Attacks

Harvest-now, decrypt-later HN-DL attacks threaten today's encrypted communications by archiving ciphertext until a quantum computer can break the underlying key exchange. This paper reframes HN-DL as an economic problem, quantifying adversary costs across Transport Layer Security TLS 1.2, TLS 1.3...

How to Maximize DDoS Readiness with Proactive Protection Strategies

Strengthen DDoS Readiness with proactive protection strategies, risk assessments, traffic monitoring, scalable defenses, and rapid response planning...

Can Adversarial Code Comments Fool AI Security Reviewers -- Large-Scale Empirical Study of Comment-Based Attacks and Defenses against LLM Code Analysis

AI-assisted code review is widely used to detect vulnerabilities before production release. Prior work shows that adversarial prompt manipulation can degrade large language model LLM performance in code generation. We test whether similar comment-based manipulation misleads LLMs during...

Intellicise Wireless Networks Meet Agentic AI: A Security and Privacy Perspective

Intellicise Intelligent and Concise wireless network is the main direction of the evolution of future mobile communication systems, a perspective now widely acknowledged across academia and industry. As a key technology within it, Agentic AI has garnered growing attention due to its advanced...

The $17 Billion Wake-Up Call: Securing Crypto in the Age of AI Scams

AI-driven crypto scams surge as cybercrime hits $17B, with deepfakes, fraud kits, and industrial social engineering reshaping digital asset threats and defenses...

Aegis: Towards Governance, Integrity, and Security of AI Voice Agents

With the rapid advancement and adoption of Audio Large Language Models ALLMs, voice agents are now being deployed in high-stakes domains such as banking, customer service, and IT support. However, their vulnerabilities to adversarial misuse still remain unexplored. While prior work has examined...