- The AI Trust Letter
- Posts
- OpenClaw Exposes Agentic AI Security Risks
OpenClaw Exposes Agentic AI Security Risks
Top AI and Cybersecurity news you should check out today
Rodrigo Fernandez
February 09, 2026
Welcome Back to The AI Trust Letter
Once a week, we distill the most critical AI & cybersecurity stories for builders, strategists, and researchers. Let’s dive in!
🚨 OpenClaw Exposes the Real Risks of Agentic AI
The Story:
OpenClaw, an open source personal AI assistant that can act through tools like email and APIs has gone viral. Its rapid adoption soon revealed serious security issues that turned it into a real world example of how agentic AI systems can be abused.
The details:
Many OpenClaw deployments exposed their control plane, known as the Gateway, directly to the public internet with no authentication. Thousands were indexed and accessible through search tools like Shodan.
Even when the Gateway was secured, attackers could still manipulate agents through prompt injection delivered via normal channels like email or chat.
In these cases, the agent was not hacked at a system level. It was tricked into misusing its own tools to exfiltrate data or perform unintended actions.
Traditional security tools failed to detect this behavior because all actions looked legitimate at the network and process level.
Why it matters:
OpenClaw shows that agentic AI introduces a new class of risk. The problem is no longer just what software runs, but why an agent takes a specific action. As agents gain autonomy and tool access, security must shift toward monitoring behavior, intent, and deviations from normal activity. This case makes clear that agent governance cannot be optional if organizations want to deploy AI agents safely at scale.
The Story:
Moltbook presents itself as “the front page of the agent internet”, a social platform built for AI agents rather than humans. Through OpenClaw, autonomous agents can join Moltbook, interact with other agents, share information, and build reputation inside a growing ecosystem that already counts more than 1.6 million active agents.
The details:
Moltbook integrates with OpenClaw via a skill that allows agents to register, authenticate, and act autonomously on the platform.
Agents can post, read content, collaborate, and respond to instructions without direct human involvement.
The onboarding flow is designed to be frictionless, using CLI commands or prompts that trigger autonomous registration workflows.
The platform relies on a heartbeat mechanism where agents periodically fetch and follow remote instructions, creating a shared coordination layer.
This architecture introduces new risks, including supply chain exposure, prompt injection through agent interactions, and agent impersonation via leaked credentials.
Why it matters:
Moltbook signals a shift from isolated AI assistants to interconnected agent ecosystems. As agents gain autonomy, internet access, and execution capabilities, traditional security assumptions break down. Platforms like this highlight the need for clear governance, agent identity controls, and continuous monitoring before social agent networks become common in enterprise and consumer environments.
🧠 Agentic AI Security Competitive Landscape Analysis
The Story:
OpenClaw is a proof of concept that shows how agentic AI systems can be manipulated to take harmful actions by chaining together tools, permissions, and context in unintended ways.
The details:
OpenClaw demonstrates how AI agents with access to tools and APIs can be steered into executing unsafe actions without exploiting traditional software vulnerabilities
The risk comes from over-permissioned agents that can plan, reason, and act across multiple steps
Many existing security controls focus on model outputs, not on the actions agents take once connected to real systems
Some early guardrails, such as tighter permission scopes, action-level logging, and kill switches for agent workflows
Why it matters:
AI sovereignty is becoming a major theme in national strategy, procurement, and regulation. But if the goal is framed as total independence, most initiatives will fail on day one. The more useful approach is to treat sovereignty as risk management: mapping critical dependencies, deciding what must be controlled locally, and building fallback plans when parts of the AI stack remain external.
🔍 Latest Claude Model Found Over 500 Software Vulnerabilities
The Story:
Anthropic reports that its newest Claude model was able to identify more than 500 previously unknown vulnerabilities while reviewing real world codebases. The exercise was designed to test whether advanced language models can meaningfully support security teams in finding flaws earlier in the development process.
The details:
Claude was used to scan large volumes of source code and flag potential security issues without prior knowledge of the projects.
The vulnerabilities ranged from common coding errors to more serious logic and security flaws that could be exploited if left unaddressed.
Anthropic positioned the model as a complement to traditional static analysis and manual review rather than a replacement for human security experts.
The results suggest that LLMs can scale security review across codebases that are too large or fast moving for manual inspection alone.
Why it matters:
This work shows how AI is starting to shift from generating code to actively auditing it. If models can reliably surface vulnerabilities at scale, security teams may be able to move detection earlier in the lifecycle. At the same time, it raises questions about accuracy, false positives, and how much trust organizations should place in automated findings without strong validation processes.
📺 Super Bowl 60 AI-Generated Ads on the Main Stage
The Story:
Super Bowl 60 featured several commercials created with the help of generative AI, marking one of the clearest signals yet that AI-assisted advertising has moved from experiments to prime-time exposure. Brands including alcohol and tech companies openly referenced their use of AI tools in the production process.
The details:
Some ads used generative models to produce visuals, scripts, or entire concepts faster and at lower cost than traditional production.
In at least one case, the brand disclosed the use of AI directly in the commercial, turning the technology itself into part of the message.
Reactions were mixed, with viewers debating originality, creative quality, and whether AI-generated content feels authentic at this scale.
Advertisers framed AI as a way to expand creative options rather than replace human teams, though the line between assistance and automation remains unclear.
Why it matters:
The Super Bowl is one of the most scrutinized advertising stages in the world. AI’s presence there signals that generative tools are becoming acceptable in mainstream creative work. It also raises questions about disclosure, authorship, and how audiences will respond as AI-generated content becomes harder to distinguish from traditional production.
What´s next?
Thanks for reading! If this brought you value, share it with a colleague or post it to your feed. For more curated insight into the world of AI and security, stay connected.
