CVE-2026-6420

A flaw was found in Keylime. An attacker with root access on an enrolled monitored machine, where the Keylime agent runs, can exploit a vulnerability in the Keylime verifier. The verifier uses a hardcoded challenge nonce for Trusted Platform Module TPM quote attestation instead of a...

CVE-2026-6420

A flaw was found in Keylime. An attacker with root access on an enrolled monitored machine, where the Keylime agent runs, can exploit a vulnerability in the Keylime verifier. The verifier uses a hardcoded challenge nonce for Trusted Platform Module TPM quote attestation instead of a...

CVE-2026-6420

A flaw was found in Keylime. An attacker with root access on an enrolled monitored machine, where the Keylime agent runs, can exploit a vulnerability in the Keylime verifier. The verifier uses a hardcoded challenge nonce for Trusted Platform Module TPM quote attestation instead of a...

PT-2026-37443

Name of the Vulnerable Software and Affected Versions Keylime affected versions not specified Description A flaw in the Keylime verifier allows an attacker with root access on an enrolled monitored machine to bypass security. The verifier uses a hardcoded challenge nonce for Trusted Platform Modu...

BIT-MLFLOW-2025-15379 Command Injection in mlflow/mlflow

A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the installmodeldependenciestoenv function. When deploying a model with envmanager=LOCAL, MLflow reads dependency specifications from the model artifact's pythonenv.yaml file and...

Arbitrary Command Injection

Overview Affected versions of this package are vulnerable to Arbitrary Command Injection in the installmodeldependenciestoenv function. An attacker can execute arbitrary commands by supplying a crafted model artifact containing malicious dependency specifications in the pythonenv.yaml file, which...

Arbitrary Command Injection

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 Arbitrary Command Injection in the installmodeldependenciestoenv...

Lean and Mean: How We Fine-Tuned a Small Language Model for Secret Detection in Code

Building an efficient small language model for cybersecurity, from data prep to deployment...

Best practices for AI security risk management

Today, we are releasing an AI security risk assessment framework as a step to empower organizations to reliably audit, track, and improve the security of the AI systems. In addition, we are providing new updates to Counterfit, our open-source tool to simplify assessing the security posture of AI...

Deployment Isn’t the Final Step – Monitoring Machine Learning Models in Production

Unless you’ve been living in a cave for the last decade, you’ve probably heard of the concept of a machine learning system at least once in your life. Whether it’s auto-translation, auto-completion, face or voice recognition, recommendation systems or autonomous driving, AI-based systems can be...