AI & LLM Vulnerabilities

CVE-2026-32207 is a cross-site scripting vulnerability (XSS, where an attacker injects malicious code into a web page that gets executed in users' browsers) in Azure Machine Learning that allows an unauthorized attacker to perform spoofing (impersonating someone or something else) over a network. The vulnerability stems from improper handling of user input during web page generation.

CVE-2026-26164 is a vulnerability in Microsoft 365 Copilot caused by improper neutralization of special elements in output (a type of injection attack, where specially crafted input can be misinterpreted as commands). An attacker without authorization could exploit this to access and disclose information over a network.

CVE-2026-26129 is a vulnerability in Microsoft 365 Copilot where improper neutralization of special elements (failure to safely handle certain characters or code) allows an unauthorized attacker to disclose information over a network. The vulnerability has a CVSS score (a 0-10 rating of how severe a vulnerability is) of 4.0, indicating moderate severity.

Diffusers, a popular AI library, had a security flaw where the `trust_remote_code` parameter (a safety check to prevent running untrusted code) could be bypassed in three ways when loading models with `DiffusionPipeline.from_pretrained()`. An attacker could execute arbitrary code on a user's machine even when the user explicitly set `trust_remote_code=False` or left it at its default safe setting. The vulnerability affected users loading custom pipelines (external code) or local model snapshots (saved model files).

The Diffusers library has a vulnerability where arbitrary code can be silently executed when loading a pipeline from HuggingFace Hub, bypassing the `trust_remote_code` security check. An attacker can craft a repository with custom code in a Python file that gets automatically executed during `DiffusionPipeline.from_pretrained()` without requiring the `trust_remote_code=True` parameter or any visible warning, allowing remote code execution (RCE, where an attacker runs commands on a system they don't own).

Keras has a critical vulnerability in its model loader (KerasFileEditor) that allows attackers to cause a Denial of Service (DoS, where a system becomes unusable) by uploading malicious .keras files. An attacker can craft a small .keras file (100-400 KB) that declares an extremely large dataset shape in its HDF5 weight file (a binary format for storing weights in neural networks), but stores only a few bytes of actual data. When Keras loads this file, it attempts to allocate petabytes of RAM based on the declared shape, immediately crashing the system and killing any applications processing the model.

The Azure authentication extension in OpenTelemetry Collector has a critical flaw where it compares bearer tokens (credentials that prove you are who you claim to be) as plain text strings instead of validating them as JWTs (JSON Web Tokens, a standard secure token format). This allows attackers who obtain a valid Azure token to reuse it indefinitely by setting the correct Host header, bypassing authentication entirely.

PraisonAI contains an unauthenticated remote code execution (RCE, where an attacker can run arbitrary commands on a server) vulnerability in the `tool_override.py` file that was missed during a previous security patch (CVE-2026-40287). An attacker can trigger this by sending a POST request to `/v1/recipes/run` with a malicious recipe, causing the server to execute a `tools.py` file without any authentication or security checks. The vulnerability affects version 4.6.31 and other recent versions.

The Network-AI project has a critical vulnerability where its MCP HTTP endpoint (a server that handles tool requests) accepts requests without any authentication checks, and binds to 0.0.0.0 (making it accessible from any network). This allows anyone who can reach the server to call privileged tools that can read and modify the system's configuration, control agents, create security tokens, and adjust budget limits.