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.
As the adoption of Large Language Models (LLMs) accelerates across industries—from customer service to legal advisory, healthcare, and finance—supply chain integrity has emerged as a cornerstone for trustworthy, secure, and scalable AI deployment. Unlike traditional software development, the LLM supply chain encompasses training datasets, pre-trained models, fine-tuning techniques, and deployment infrastructures—all of which are susceptible to unique attack vectors.
When deploying LLMs in enterprise environments, overlooking secure configuration practices can unintentionally expose sensitive backend logic, security parameters, or operational infrastructure. These misconfigurations—often subtle—can offer attackers or misinformed users unintended access to the LLM’s internal behaviour, leading to serious data leakage and system compromise.