Toward Securing AI Agents like Operating Systems

Autonomous agents based on large language models LLMs are rapidly emerging as a general-purpose technology, with recent systems such as OpenClaw extending their capabilities through broad tool use, third-party skills, and deeper integration into user environments. At the same time, these agentic...

MATRA: Modeling the Attack Surface of Agentic AI Systems -- OpenClaw Case Study

LLMs are increasingly deployed as autonomous agents with access to tools, databases, and external services, yet practitioners across different sectors lack systematic methods to assess how known threat classes translate into concrete risks within a specific agentic deployment. We present MATRA, a...

Redefining AI Red Teaming in the Agentic Era: From Weeks to Hours

AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary defense, current approaches force operators into manual, library-specific workflows. Operators spend weeks hand-crafting workflows -...

Security Attack and Defense Strategies for Autonomous Agent Frameworks: A Layered Review with OpenClaw As a Case Study

Autonomous agent frameworks built upon large language models LLMs are evolving into complex, tool-integrated, and continuously operating systems, introducing security risks beyond traditional prompt-level vulnerabilities. As this paradigm is still at an early stage of development, a timely and...

PT-2026-33634

Name of the Vulnerable Software and Affected Versions UltraDAG version 0.1 Description A non-council attacker can submit a signed 'SmartOp::Vote' transaction that successfully passes signature, nonce, and balance prechecks. However, the authorization check fails only after state mutation has...

SoK: Reshaping Research on Network Intrusion Detection Systems

Network Intrusion Detection Systems NIDS have been studied for decades. Hundreds of papers have, e.g., proposed ways to enhance, harden or bypass NIDS. However, the findings of prior literature are hardly reflected in real-world operational contexts. Such a disconnection is problematic for resear...

A LINDDUN-Based Privacy Threat Modeling Framework for GenAI

As generative AI GenAI systems become increasingly prevalent across various technological stacks, the question of how such systems handle sensitive and personal data flows becomes increasingly important. Specifically, both the ability to harness and process large swaths of information as well as...

Evaluating the Reliability of Digital Forensic Evidence Discovered by Large Language Model: A Case Study

The growing reliance on AI-identified digital evidence raises significant concerns about its reliability, particularly as large language models LLMs are increasingly integrated into forensic investigations. This paper proposes a structured framework that automates forensic artifact extraction,...

Orbital Escalation: Modeling Satellite Ransomware Attacks Using Game Theory

Ransomware has yet to reach orbit, but the conditions for such an attack already exist. This paper presents the first game-theoretic framework for modeling ransomware against satellites: the orbital escalation game. In this model, the attacker escalates ransom demands across orbital passes, while...

[Webinar] The Smarter SOC Blueprint: Learn What to Build, Buy, and Automate

Most security teams today are buried under tools. Too many dashboards. Too much noise. Not enough real progress. Every vendor promises “complete coverage” or “AI-powered automation,” but inside most SOCs, teams are still overwhelmed, stretched thin, and unsure which tools are truly pulling their...

Case study: Securing AI application supply chains

The rapid adoption of AI applications, including agents, orchestrators, and autonomous workflows, represents a significant shift in how software systems are built and operated. Unlike traditional applications, these systems are active participants in execution. They make decisions, invoke tools,...

Toward Risk Thresholds for AI-Enabled Cyber Threats: Enhancing Decision-Making under Uncertainty with Bayesian Networks

Artificial intelligence AI is increasingly being used to augment and automate cyber operations, altering the scale, speed, and accessibility of malicious activity. These shifts raise urgent questions about when AI systems introduce unacceptable or intolerable cyber risk, and how risk thresholds...

AttackMate: Realistic Emulation and Automation of Cyber Attack Scenarios across the Kill Chain

Adversary emulation tools facilitate scripting and automated execution of cyber attack chains, thereby reducing costs and manual expert effort required for security testing, cyber exercises, and intrusion detection research. However, due to the fact that existing tools typically rely on agents...

ReSMT: An SMT-Based Tool for Reverse Engineering

Software obfuscation techniques make code more difficult to understand, without changing its functionality. Such techniques are often used by authors of malicious software to avoid detection. Reverse Engineering of obfuscated code, i.e., the process of overcoming obfuscation and answering questio...

Rethinking Cybersecurity Ontology Classification and Evaluation: Towards a Credibility-Centered Framework

This paper analyzes the proliferation of cybersecurity ontologies, arguing that this surge cannot be explained solely by technical shortcomings related to quality, but also by a credibility deficit - a lack of trust, endorsement, and adoption by users. This conclusion is based on our first...

A Safety and Security Framework for Real-World Agentic Systems

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrator...

Improving Cybercrime Detection and Digital Forensics Investigations with Artificial Intelligence

According to a recent EUROPOL report, cybercrime is still recurrent in Europe, and different activities and countermeasures must be taken to limit, prevent, detect, analyze, and fight it. Cybercrime must be prevented with specific measures, tools, and techniques, for example through automated...

Dynamic Causal Attack Graph Based Cyber-Security Risk Assessment Framework for CTCS System

Protecting the security of the train control system is a critical issue to ensure the safe and reliable operation of high-speed trains. Scientific modeling and analysis for the security risk is a promising way to guarantee system security. However, the representation and assessment of the...

Binary Diff Summarization Using Large Language Models

Security of software supply chains is necessary to ensure that software updates do not contain maliciously injected code or introduce vulnerabilities that may compromise the integrity of critical infrastructure. Verifying the integrity of software updates involves binary differential analysis...

STAF: Leveraging LLMs for Automated Attack Tree-Based Security Test Generation

In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive,...