AI Sec Watch

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.

Gradio is an open-source Python package for building machine learning demos and web applications. Before version 5.31.0, a vulnerability in its flagging feature let unauthenticated attackers copy any readable file from the server's filesystem, which could cause DoS (denial of service, where a system becomes unavailable) by copying massive files to fill up disk space, though attackers couldn't actually read the copied files.

CVE-2025-48491 is a vulnerability in Project AI, a platform for creating AI agents, where a hardcoded API key (a secret credential stored directly in the code rather than kept separate) was exposed in versions before the pre-beta release. This means attackers could potentially find and misuse this key to access the system without proper authorization.

vLLM (a system for running large language models) versions 0.7.0 through 0.8.x have a bug in how they create hash values (fingerprints) for images. The hashing method only looks at the raw pixel data and ignores important image properties like width and height, so two different-sized images with the same pixels would create identical hash values. This can cause the system to incorrectly reuse cached results or expose data it shouldn't.