Overview
Ollama’s model quantization engine contains a vulnerability that allows an attacker with access to the model upload interface to read and potentially exfiltrate heap memory from the server. This issue may lead to unintended behavior, including unauthorized access to sensitive data and, in some cases, broader system compromise.
Description
Ollama is an open-source tool designed to run large language models (LLMs) locally on personal systems, including macOS, Windows, and Linux. Ollama supports model quantization, an optimization technique that reduces the numerical precision used in models to improve performance and efficiency.
An out-of-bounds heap read/write vulnerability has been identified in Ollama’s model processing engine. By uploading a specially crafted GPT-Generated Unified Format (GGUF) file and triggering the quantization process, an attacker can cause the server to read beyond intended memory boundaries and write the leaked data into a new model layer.
CVE-2026-5757: Unauthenticated remote information disclosure vulnerability in Ollama’s model quantization engine allows an attacker to read and exfiltrate the server’s heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence.
The vulnerability is caused by three combined factors:

No Bounds Checking: The quantization engine trusts tensor metadata (like element count) from the user-supplied GGUF file header without verifying it against the actual size of the provided data.
Unsafe Memory Access: Go’s unsafe.Slice is used to create a memory slice based on the attacker-controlled element count, which can extend far beyond the legitimate data buffer and into the application’s heap.
Data Exfiltration Path: The out-of-bounds heap data is inadvertently processed and written into a new model layer. Ollama’s registry API can then be used to “push” this layer to an attacker-controlled server, effectively exfiltrating the leaked memory.

Impact
An attacker with access to the model upload interface can exploit this vulnerability to read from or write to heap memory. This may result in exposure of sensitive data, data exfiltration, and potentially full system compromise.
Solution
Unfortunately, we were unable to reach the vendor to coordinate this vulnerability, and a patch is not yet available to address this vulnerability. The underlying issue should be addressed by implementing proper bounds checking to ensure that tensor metadata is validated against the actual size of the provided data before any memory operations are performed.
As an interim mitigation, access to the model upload functionality should be restricted or disabled, particularly in environments exposed to untrusted users or networks. Deployments should be limited to local or otherwise trusted network environments where possible. If model uploads are required for operational reasons, only models from trusted and verifiable sources should be accepted, and appropriate validation controls should be applied to reduce risk.
Acknowledgements
Thanks to the reporter Jeremy Brown, who detected the vulnerability through AI-assisted vulnerability research. This document was written by Timur Snoke.

Overview
Radware Alteon has a reflected Cross-Site Scripting (XSS) vulnerability in the parameter ReturnTo of the route /protected/login. This vulnerability allows an attacker to execute JavaScript in the host browser.
Description
CVE-2026-5754: Reflected Cross-Site Scripting (XSS) vulnerability in Radware Alteon 34.5.4.0 vADC load-balancer allows an attacker to inject malicious scripts into the website, potentially leading to unauthorized actions, data theft, or other malicious activities.
A reflected Cross-Site Scripting (XSS) vulnerability exists in the ReturnTo parameter of the /protected/login route in Radware Alteon version 34.5.4.0. The vulnerability arises from the lack of user input sanitization, allowing an attacker to inject malicious scripts. Specifically, when a user requests a resource that redirects to a Microsoft SAML login page, the load-balancer redirects the user to the login page with a ReturnTo parameter that fails to sanitize user input. An attacker can exploit this by injecting a malicious payload in the ReturnTo parameter, which will be executed in the victim’s browser.
An example attack flow is below:

Attacker creates link with XSS payload in ReturnTo parameter.
Victim clicks malicious link, redirecting to login page.
Load-balancer reflects malicious ReturnTo parameter, executing XSS payload.
Attacker performs JavaScript code execution in the victim’s browser.

Impact
The impact of this vulnerability is significant, as it allows an attacker to execute arbitrary JavaScript code in a victim’s browser. Doing so enables a range of harmful activities, including the following: stealing session cookies and sensitive data; performing unauthorized actions on behalf of the victim; tricking users into falling for phishing attacks; and damaging a website’s reputation and user trust.
Solution
Unfortunately, we were unable to reach the vendor to coordinate this vulnerability. The vendor, Radware, has acknowledged the vulnerability in their customer portal and plans to patch it in the next version, 34.5.7.0. This version was expected to be released on March 31st, 2026, based upon the release notes, but it is unclear if this release occurred and included a fix. In the meantime, users are advised to take precautions to prevent exploitation, such as validating and encoding user input.
Acknowledgements
Thanks to the reporter, Loinaz Merino Cerrajeria, for bringing this vulnerability to our attention.This document was written by Timur Snoke.

Overview
Terrarium is a sandbox-based code execution platform that enables users to run and execute code in a controlled environment, providing a secure way to test and validate code. However, a vulnerability has been discovered in Terrarium that allows arbitrary code execution with root privileges on the host Node.js process. This vulnerability is caused by a JavaScript prototype chain traversal in the Pyodide WebAssembly environment.
Description
The root cause of the vulnerability lies in the configuration of jsglobals objects in service.ts. Specifically, the mock document object is created using a standard JavaScript object literal, which inherits properties from Object.prototype. This inheritance chain allows sandbox code to traverse up to the function constructor, create a function that returns globalThis, and from there access Node.js internals, including require(). As a result, an attacker can escape the sandbox and execute arbitrary system commands as root within the container.
CVE-2026-5752
Sandbox Escape Vulnerability in Terrarium allows arbitrary code execution with root privileges on a host process via JavaScript prototype chain traversal.
Impact
Applications that use Terrarium for sandboxed code execution may be compromised, allowing an attacker to:

Execute arbitrary commands as root inside the container
Access and modify sensitive files, including /etc/passwd and environment variables
Reach other services on the container’s network, including databases and internal APIs
Potentially escape the container and escalate privileges further

Mitigation
Unfortunately, we were unable to coordinate with the vendor to obtain a patch or fix for this vulnerability. In the meantime, several mitigation strategies can be employed to reduce the risk of exploitation. Users should consider implementing the following measures:

Disable unnecessary features: Disable any features that allow users to submit code to the sandbox, if possible.
Implement network segmentation: Segment the network to limit the attack surface and prevent lateral movement.
Use a Web Application Firewall (WAF): Deploy a WAF to detect and block suspicious traffic, including attempts to exploit the vulnerability.
Monitor container activity: Regularly monitor container activity for signs of suspicious behavior.
Implement access controls: Limit access to the container and its resources to authorized personnel only.
Use a secure container orchestration tool: Utilize a secure container orchestration tool to manage and secure containers.
Regularly update and patch dependencies: Ensure that dependencies are up-to-date and patched.

Acknowledgments
The vulnerability was discovered by Jeremy Brown, who used AI-assisted vulnerability research to identify the issue. This document was written by Timur Snoke with assistance from AI.