AI Sec Watch

CosyVoice, a text-to-speech framework, has an insecure deserialization vulnerability (CWE-502, a weakness where untrusted data is converted back into executable objects) in how it loads model files. The vulnerability exists because the code uses torch.load() without the weights_only=True security setting, allowing attackers to execute arbitrary code (malicious instructions) on a victim's computer by tricking them into loading a specially crafted model file through the CosyVoice Web UI.

CosyVoice (a speech synthesis tool) has an insecure deserialization vulnerability (CWE-502, a flaw where untrusted data is converted back into executable code) in its gRPC server (a framework for building networked services). The vulnerability occurs because the server uses torch.load() without the weights_only=True parameter to load speech models, allowing an attacker to execute arbitrary code by placing malicious model files in a directory that a victim then loads.

CosyVoice, a voice synthesis tool, has a vulnerability in its model averaging feature where it loads PyTorch checkpoint files (serialized machine learning model files) using an unsafe method that can execute arbitrary code. An attacker can create malicious checkpoint files that, when processed by the tool, will run code on the victim's computer without permission.

CosyVoice (a text-to-speech AI tool) has a vulnerability in how it loads PyTorch model files (machine learning data files containing voice embeddings and tokens). The tool uses an unsafe loading method that allows attackers to execute arbitrary code (run any commands they want) on a victim's computer if the victim processes a directory containing a malicious .pt file.

GPT-Pilot has a command injection vulnerability (CWE-78, a type of security flaw where attackers insert malicious commands into a program) in its Executor.run() method that allows attackers to execute arbitrary shell commands. When the system asks users to confirm or modify a command before running it, it doesn't properly validate the user input before passing it to the shell execution function, letting an attacker replace the intended command with malicious code and run it with GPT-Pilot's privileges.

Google discovered and blocked a zero-day exploit (a previously unknown security flaw) that was created with AI assistance, which criminals planned to use for mass attacks on a web administration tool. Researchers identified AI involvement by finding signs in the Python script like artificial CVSS scores (severity ratings) and text patterns typical of AI language models.

OpenAI's Q1 2026 data shows ChatGPT adoption expanded beyond early adopters, with growing usage among older age groups, users with typically feminine names, and people in emerging markets across Latin America, Asia-Pacific, and Africa. Workplace use evolved to focus on specialized tasks like content creation and health documentation rather than just general writing. Overall, ChatGPT became a more mainstream tool used by diverse people in more countries for recurring tasks.

go-git (a Git implementation in Go) may parse malformed Git objects differently than upstream Git, which could cause commits or tags with ambiguous headers to be interpreted inconsistently. This is especially problematic for commit signing and verification, since go-git signs or verifies commits based on its own parsed representation rather than the original raw bytes, potentially making invalid signatures appear valid when the commit's displayed content differs from what was actually signed.

According to Google's threat intelligence group, AI-powered hacking (using AI models to help create and scale cyberattacks) has rapidly grown from a minor issue to a large, organized threat in just three months. Criminal groups and state-sponsored actors are now using commercial AI models to write code and find vulnerabilities (weaknesses in software that can be exploited) more effectively and at a much larger scale.