Supply-chain threats no longer pertain solely to enterprise software. In the world of on-device LLMs, hardware, firmware, model packaging, and even manufacturing processes are all potential vectors for compromise.
The success of platforms like Hugging Face, GitHub, and Weights & Biases demonstrates a strong appetite for collaborative AI development. Organisations often benefit from open-sourcing internal models, using pre-trained datasets, or merging models to create new capabilities faster.
Kali GPT is an advanced AI system built on top of the Kali Linux penetration testing distribution. It utilises large language models (LLMs) and offensive security modules to assist penetration testers in automating reconnaissance, exploitation, privilege escalation, and post-exploitation tasks.
Weak Model Provenance: Trust Without Proof A critical weakness in today’s AI model landscape is the lack of strong provenance mechanisms. While tools like Model Cards and accompanying documentation attempt to offer insight into a model’s architecture, training data, and intended use cases, they fall short of providing cryptographic or verifiable proof of the model’s …
Pre-trained models are widely adopted for their ability to accelerate AI deployments and reduce development costs. However, this convenience comes at a hidden price: they introduce vulnerabilities that can silently compromise entire systems. Whether sourced from reputable repositories or lesser-known vendors, these models can harbour biases, backdoors, or outright malicious behaviours—threats that are difficult to detect and even harder to mitigate post-deployment.