AI Sec Watch

Codex (an AI coding assistant) agent loops involved many back-and-forth API requests that added significant delays, especially as model inference speeds improved to nearly 1,000 tokens per second (words generated per second). To reduce this overhead, the team implemented WebSockets (a protocol that maintains a persistent connection between client and server, rather than opening a new connection for each request), along with caching and eliminating unnecessary network calls, achieving a 40% overall speedup in end-to-end performance.

OpenAI has introduced workspace agents in ChatGPT, which are AI tools that can handle complex work tasks and long-running workflows while respecting organizational permissions and controls. These agents, powered by Codex (a code-generating AI model), can automate tasks like report writing, code generation, and message responses, and can continue working in the cloud even when users are offline. Teams can create shared agents once and reuse them across ChatGPT and Slack, with examples including agents that review software requests, route product feedback, and manage vendor risk assessment.

Workspace agents are AI systems designed to automate repeatable workflows in your daily work by connecting to tools your team uses, rather than helping with one-off tasks. A workspace agent has three core components: a trigger (what starts it, like a schedule), a process with specialized skills (the steps it follows), and access to tools or systems (like Slack or a CRM). Unlike traditional deterministic workflows (where each step is explicitly defined and always the same), agents are probabilistic, meaning they use AI to interpret context and adjust their approach while staying within set instructions and guardrails.

Anthropic's Mythos AI model, a tool designed to find security weaknesses in software, was accessed by unauthorized users through a private online forum using a contractor's credentials and basic internet research techniques. The model is capable of identifying and exploiting vulnerabilities (security flaws) in major operating systems and web browsers, which is why Anthropic warned it could be dangerous if misused.

AI tools like Anthropic's Mythos can find software vulnerabilities much faster than before, creating a problem: security teams must decide which vulnerabilities to fix first among thousands of options. Anthropic recommends using EPSS (Exploit Prediction Scoring System, a machine learning model that predicts which vulnerabilities are likely to be exploited in the next 30 days) to prioritize which vulnerabilities need immediate attention, similar to how weather forecasters predict whether you'll need an umbrella.

Anthropic is investigating a report that unauthorized users gained access to Mythos, an AI model designed to detect cybersecurity vulnerabilities that the company has kept private because it could be misused to enable cyber-attacks. A small group of people allegedly accessed the model without permission, prompting the company to look into the incident.

Terrarium, a Python sandbox developed by Cohere AI for running untrusted code in containers, has a critical vulnerability (CVE-2026-5752, CVSS 9.3) that allows attackers to execute arbitrary code with root privileges through JavaScript prototype chain traversal (a technique where attackers manipulate how JavaScript looks up object properties to access restricted functionality). Since the project is no longer maintained, a patch is unlikely, but CERT/CC recommends several defensive measures.

GitHub Copilot changed its pricing and usage limits for individual users because agentic workflows (AI agents that run long tasks automatically) consume far more computing resources than expected, with some users burning tokens (units of text processed by the AI) at much higher rates than before. The changes include pausing new individual plan signups, moving the most advanced Claude Opus 4.7 model to a more expensive $39/month tier, and switching to token-based usage limits tracked per session and per week instead of per-request charging.

This paper proposes a framework for teaching AI skills to business students, arguing that universities should prepare graduates to lead AI-driven business transformation. The framework outlines seven competency areas, including AI literacy, governance and risk management, technology management, systems development, and AI strategy, to guide the design of business school programs that help students develop, deploy, and use AI solutions effectively in organizations.