CVE-2026-2473 Bucket Squatting in Vertex AI Experiments leads to RCE and Model Theft.

Predictable bucket naming in Vertex AI Experiments in Google Cloud Vertex AI from version 1.21.0 up to but not including 1.133.0 on Google Cloud Platform allows an unauthenticated remote attacker to achieve cross-tenant remote code execution, model theft, and poisoning via pre-creating predictabl...

CVE-2026-2473 Bucket Squatting in Vertex AI Experiments leads to RCE and Model Theft.

Predictable bucket naming in Vertex AI Experiments in Google Cloud Vertex AI from version 1.21.0 up to but not including 1.133.0 on Google Cloud Platform allows an unauthenticated remote attacker to achieve cross-tenant remote code execution, model theft, and poisoning via pre-creating predictabl...

PT-2026-21291

Name of the Vulnerable Software and Affected Versions Google Cloud Vertex AI versions 1.21.0 through 1.132.9 Description A flaw exists in Vertex AI Experiments within Google Cloud Vertex AI that could allow a remote, unauthenticated attacker to execute code, steal models, and poison data. This is...

Server-side Request Forgery (SSRF)

Overview Affected versions of this package are vulnerable to Server-side Request Forgery SSRF via the HttpUriPlugin component when HTTP redirects are followed without re-validating the allowed URIs. An attacker can cause unauthorized network requests to internal services and inclusion of untruste...

Server-side Request Forgery (SSRF)

Overview Affected versions of this package are vulnerable to Server-side Request Forgery SSRF via the HttpUriPlugin component when HTTP redirects are followed without re-validating the allowed URIs. An attacker can cause unauthorized network requests to internal services and inclusion of untruste...

CVE-2025-14279

MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. An...

GHSA-PGQP-8H46-6X4J MLFlow is vulnerable to DNS rebinding attacks due to a lack of Origin header validation

MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. An...

Origin Validation Error

Overview mlflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. Affected versions of this package are vulnerable to Origin Validation Error in the REST server, accessible via the...

CVE-2025-14279 DNS Rebinding Vulnerability in mlflow/mlflow

MLFlow versions up to and including 3.4.0 are vulnerable to DNS rebinding attacks due to a lack of Origin header validation in the MLFlow REST server. This vulnerability allows malicious websites to bypass Same-Origin Policy protections and execute unauthorized calls against REST endpoints. An...

CVE-2025-14279

The CVE details a DNS rebinding vulnerability in MLflow up to version 3.4.0 caused by lack of Origin header validation in the MLflow REST server. The issue allows an attacker to bypass Same-Origin Policy and issue unauthorized requests to REST endpoints, enabling querying, updating, and deleting ...

PT-2026-1734

Name of the Vulnerable Software and Affected Versions MLFlow versions up to and including 3.4.0 Description MLFlow versions up to and including 3.4.0 are susceptible to DNS rebinding attacks because of missing Origin header validation within the MLFlow REST server. This allows malicious websites ...

CVE-2026-22713

Improper Neutralization of Input During Web Page Generation XSS or 'Cross-site Scripting' vulnerability in The Wikimedia Foundation Mediawiki - GrowthExperiments Extension allows Cross-Site Scripting XSS.This issue affects Mediawiki - GrowthExperiments Extension: 1.45, 1.44, 1.43, 1.39...

CVE-2022-0784

The Title Experiments Free WordPress plugin before 9.0.1 does not sanitise and escape the id parameter before using it in a SQL statement via the wpextitles AJAX action available to unauthenticated users, leading to an unauthenticated SQL injection...

CVE-2023-4018

An issue has been discovered in GitLab affecting all versions starting from 16.2 before 16.2.5, all versions starting from 16.3 before 16.3.1. Due to improper permission validation it was possible to create model experiments in public projects...

CVE-2026-22713

Improper Neutralization of Input During Web Page Generation XSS or 'Cross-site Scripting' vulnerability in The Wikimedia Foundation Mediawiki - GrowthExperiments Extension allows Cross-Site Scripting XSS.This issue affects Mediawiki - GrowthExperiments Extension: 1.45, 1.44, 1.43, 1.39...

CVE-2026-22713

The CVE-2026-22713 issue affects MediaWiki - GrowthExperiments Extension versions 1.39, 1.43, 1.44, and 1.45, where improper neutralization of input during web page generation enables Cross-Site Scripting (XSS). The vulnerability stems from how edit summaries are processed, allowing injected scri...

The vulnerability of the Rockwell Automation Arena Simulation software for simulating and automating discrete events lies in buffer overflows in the stack. This allows a hacker to execute arbitrary code.

The vulnerability of the Rockwell Automation Arena Simulation software for simulating and automating discrete events is related to buffer overflow in the stack. Exploiting this vulnerability can allow an attacker to execute arbitrary code using specially created DOE files...

A Descriptive Model for Modelling Attacker Decision-Making in Cyber-Deception

Cyber-deception is an increasingly important defensive strategy, shaping adversarial decision making through controlled misinformation, uncertainty, and misdirection. Although game-theoretic, Bayesian, Markov decision process, and reinforcement learning models offer insight into deceptive...

Scaling Patterns in Adversarial Alignment: Evidence from Multi-LLM Jailbreak Experiments

Large language models LLMs increasingly operate in multi-agent and safety-critical settings, raising open questions about how their vulnerabilities scale when models interact adversarially. This study examines whether larger models can systematically jailbreak smaller ones - eliciting harmful or...

CYPRESS: Transferring Secrets in the Shadow of Visible Packets

Network steganography and covert communication channels have been studied extensively in the past. However, prior works offer minimal practical use for their proposed techniques and are limited to specific use cases and network protocols. In this paper, we show that covert channels in networking...