OpenClaw, Moltbook, and ClawdLab: From Agent-Only Social Networks to Autonomous Scientific Research

In January 2026, the open-source agent framework OpenClaw and the agent-only social network Moltbook produced a large-scale dataset of autonomous AI-to-AI interaction, attracting six academic publications within fourteen days. This study conducts a multivocal literature review of that ecosystem a...

Agents of Chaos

We report an exploratory red-teaming study of autonomous language-model-powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents unde...

Malicious AI

Interesting: Summary: An AI agent of unknown ownership autonomously wrote and published a personalized hit piece about me after I rejected its code, attempting to damage my reputation and shame me into accepting its changes into a mainstream python library. This represents a first-of-its-kind cas...

Assessing Cybersecurity Risks and Traffic Impact in Connected Autonomous Vehicles

Given the promising future of autonomous vehicles, it is foreseeable that self-driving cars will soon emerge as the predominant mode of transportation. While autonomous vehicles offer enhanced efficiency, they remain vulnerable to external attacks. In this research, we sought to investigate the...

Google Links China, Iran, Russia, North Korea to Coordinated Defense Sector Cyber Operations

Several state-sponsored actors, hacktivist entities, and criminal groups from China, Iran, North Korea, and Russia have trained their sights on the defense industrial base DIB sector, according to findings from Google Threat Intelligence Group GTIG. The tech giant's threat intelligence division...

In-Context Autonomous Network Incident Response: An End-To-End Large Language Model Agent Approach

Rapidly evolving cyberattacks demand incident response systems that can autonomously learn and adapt to changing threats. Prior work has extensively explored the reinforcement learning approach, which involves learning response strategies through extensive simulation of the incident. While this...

Hand over the keys for Shannon’s shenanigans

Welcome to this week's edition of the Threat Source newsletter. Last week, yet another security AI tool made the rounds on social media: Shannon, a fully autonomous AI penetration testing tool created by Keygraph. It "autonomously hunts for attack vectors in your code, then uses its built-in...

Agentic AI for Cybersecurity: A Meta-Cognitive Architecture for Governable Autonomy

Contemporary AI-driven cybersecurity systems are predominantly architected as model-centric detection and automation pipelines optimized for task-level performance metrics such as accuracy and response latency. While effective for bounded classification tasks, these architectures struggle to...

Prompt Injection Via Road Signs

Interesting research: "CHAI: Command Hijacking Against Embodied AI." Abstract: Embodied Artificial Intelligence AI promises to handle edge cases in robotic vehicle systems where data is scarce by using common-sense reasoning grounded in perception and action to generalize beyond training...

Agentic Knowledge Distillation: Autonomous Training of Small Language Models for SMS Threat Detection

SMS-based phishing smishing attacks have surged, yet training effective on-device detectors requires labelled threat data that quickly becomes outdated. To deal with this issue, we present Agentic Knowledge Distillation, which consists of a powerful LLM acts as an autonomous teacher that fine-tun...

QRS: A Rule-Synthesizing Neuro-Symbolic Triad for Autonomous Vulnerability Discovery

Static Application Security Testing SAST tools are integral to modern DevSecOps pipelines, yet tools like CodeQL, Semgrep, and SonarQube remain fundamentally constrained: they require expert-crafted queries, generate excessive false positives, and detect only predefined vulnerability patterns...

Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectors

This article investigates the robustness of vision systems in Connected and Autonomous Vehicles CAVs, which is critical for developing Level-5 autonomous driving capabilities. Safe and reliable CAV navigation undeniably depends on robust vision systems that enable accurate detection of objects,...

SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity

Deep Reinforcement Learning DRL has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, transitioning DRL from laboratory simulations to bespoke cyber environments can introduce numerous issues. This is furthe...

CyberExplorer: Benchmarking LLM Offensive Security Capabilities in a Real-World Attacking Simulation Environment

Real-world offensive security operations are inherently open-ended: attackers explore unknown attack surfaces, revise hypotheses under uncertainty, and operate without guaranteed success. Existing LLM-based offensive agent evaluations rely on closed-world settings with predefined goals and binary...

Vajra

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Viral AI, Invisible Risks: What OpenClaw Reveals About Agentic Assistants

OpenClaw aka Clawdbot or Moltbot represents a new frontier in agentic AI: powerful, highly autonomous, and surprisingly easy to use. In this research, we examine how its capabilities compare to its predecessors’ and highlight the security risks inherent to the agentic AI paradigm...

Beyond Crash: Hijacking Your Autonomous Vehicle for Fun and Profit

Autonomous Vehicles AVs, especially vision-based AVs, are rapidly being deployed without human operators. As AVs operate in safety-critical environments, understanding their robustness in an adversarial environment is an important research problem. Prior physical adversarial attacks on vision-bas...

All gas, no brakes: Time to come to AI church

Welcome to this week's edition of the Threat Source newsletter. Brothers and sisters, gather close for a moment. We are all security followers here gathered in fellowship and community, with one joyful spirit to fight the good fight and do good out there in the security world. It is with that...

Toxic_Flow_Analysis_Framework_For_Agentic_AI

Toxic Flow Analysis TFA Framework A Secure-by-Design framew...

Winning Against AI-Based Attacks Requires a Combined Defensive Approach

If there's a constant in cybersecurity, it's that adversaries are always innovating. The rise of offensive AI is transforming attack strategies and making them harder to detect. Google's Threat Intelligence Group, recently reported on adversaries using Large Language Models LLMs to both conceal...