AI Sec Watch

The mcp-com-server is a tool that connects the Model Context Protocol (MCP, a standard for AI systems to interact with external tools) to COM (Component Object Model, Microsoft's decades-old system for sharing functionality across programs on Windows). This allows an AI like Claude to automate Windows and Office tasks, such as creating Excel files and sending emails, by dynamically discovering and controlling COM objects. The main security risk is that COM can access dangerous operations like file system access, so the server uses an allowlist (a list of approved COM objects that are permitted to run) to restrict which COM objects can be instantiated.

Skyvern through version 0.1.85 has a vulnerability where attackers can inject malicious code into the Prompt field of workflow blocks through SSTI (server-side template injection, where untrusted input is processed as code by the server's template engine). Authenticated users can craft special expressions in Jinja2 templates (a template system that evaluates code on the server) that aren't properly cleaned up, allowing them to execute commands on the server without direct feedback, a capability known as blind RCE (remote code execution).

The Hive Support plugin for WordPress has a security flaw in versions up to 1.2.4 where two functions lack capability checks (security checks that verify user permissions). This allows attackers with basic Subscriber-level accounts to read and change the site's OpenAI API key, inspect data, and modify how the AI chatbot behaves.

This content is a collection of blog post titles and announcements from Palo Alto Networks about AI security, covering topics like agentic AI (AI systems that can autonomously take actions), container security, and operational technology (OT, the systems that control physical infrastructure) security. The posts discuss vulnerabilities in autonomous AI systems, the need for contextual red teaming (security testing tailored to specific use cases), and various security products like Prisma AIRS.

AstrBot, a chatbot and development framework powered by large language models (LLMs, AI systems trained on large amounts of text data), has a path traversal vulnerability (a flaw that lets attackers access files they shouldn't be able to reach) in versions 3.4.4 through 3.5.12 that could expose sensitive information like API keys (credentials used to access external services) and passwords. The vulnerability was fixed in version 3.5.13.

vLLM (a system for running and serving large language models) versions 0.8.0 through 0.9.0 have a vulnerability where the /v1/chat/completions API endpoint doesn't properly check user input in the 'pattern' and 'type' fields when the tools feature is used, allowing a single malformed request to crash the inference worker (the part that actually runs the model) until someone restarts it.

CVE-2025-48943 is a Denial of Service vulnerability (a type of attack that crashes a system) in vLLM versions 0.8.0 through 0.8.x that causes the server to crash when given an invalid regex (a pattern used to match text). This happens specifically when using the structured output feature, which lets the AI format responses in a specific way.

vLLM (an inference and serving engine for large language models) versions 0.8.0 through 0.8.x have a vulnerability where sending an invalid JSON schema as a parameter to the /v1/completions API endpoint causes the server to crash. This happens because the application doesn't properly handle (catch) exceptions that occur when processing malformed input.

vLLM, a software system that runs and serves large language models, has a vulnerability in how it parses tool commands that can be exploited to crash or slow down the service. The problem comes from using an overly complex pattern-matching rule (regular expression with nested quantifiers, optional groups, and inner repetitions) that can cause the system to get stuck processing certain inputs, leading to severe performance problems.