AI Sec Watch

Nation-states are using AI agents (autonomous AI systems that can perform tasks without human intervention) to launch cyberattacks at speeds that traditional security responses cannot match. The article argues that cybersecurity defenses cannot rely on small, gradual improvements but must instead undergo fundamental architectural changes to address this new threat level.

Trent AI, a new startup, has secured $13 million in funding to develop a layered security solution (a multi-level protective system) designed to protect AI agents (software programs that act autonomously to complete tasks) throughout their entire lifecycle, from creation to deployment.

Many enterprises have applications disconnected from centralized identity systems (systems that control who can access what), creating blind spots that AI agents and attackers are actively exploiting. While organizations have invested in IAM (identity and access management, the practice of controlling user access) and Zero Trust security, legacy apps and siloed systems remain outside of centralized control, allowing AI agents to amplify credential risks and bypass security oversight.

CVE-2026-35487 is a path traversal vulnerability (a flaw that lets attackers read files outside the intended directory) in text-generation-webui, an open-source tool for running large language models through a web interface. Before version 4.3, attackers could exploit the load_prompt() function without logging in to read any .txt file on the server and see its contents in the API response.

text-generation-webui, an open-source web interface for running Large Language Models, has a vulnerability in versions before 4.3 where the superbooga and superboogav2 RAG extensions (tools that fetch external documents to help answer questions) accept user-provided URLs without checking them for safety. This allows attackers to access cloud metadata endpoints (services that store sensitive credentials in cloud environments) and steal IAM credentials (identity and access management tokens that control what users can do). The vulnerability is fixed in version 4.3.

text-generation-webui, an open-source web interface for running Large Language Models, has a path traversal vulnerability (a security flaw where an attacker can access files outside the intended directory) in versions before 4.3. An unauthenticated attacker can exploit this by sending specially crafted requests through the API to read any file on the server, because Gradio (the framework it uses) does not validate user input on the server side.

CVE-2026-35484 is a path traversal vulnerability (a bug where an attacker can access files outside the intended folder) in text-generation-webui, an open-source tool for running large language models through a web interface. Before version 4.3, attackers could read any .yaml file (a configuration file format) on the server without needing to log in, potentially exposing sensitive data like passwords and API keys in the response.

CVE-2026-35483 is a path traversal vulnerability (a flaw that lets attackers read files outside intended directories) in text-generation-webui, an open-source tool for running large language models. Versions before 4.3 allow unauthenticated attackers to read files with extensions like .jinja, .jinja2, .yaml, or .yml from anywhere on the server.

At RSAC 2026, cybersecurity leaders discussed how AI should be used in security work, including debates about agentic applications (AI systems that can act independently to solve problems) and whether human involvement can realistically keep up as AI scales up. The discussions highlighted the tension between automating security tasks with AI and maintaining human oversight in important decisions.