AI Sec Watch

Cantina, a cybersecurity startup, announced $8 million in new funding for a platform that uses autonomous security workers (AI agents that work independently to complete tasks) to automatically find, prioritize, and fix vulnerabilities in organizations' systems. The platform learns from each investigation to become more accurate over time and provides security teams with a real-time overview of risks across their entire environment.

Discern Security, a California-based company founded in 2023, announced it raised $13 million in Series A funding for a total of $16 million raised. The company provides an AI-powered security platform that uses AI agents (AI systems that can autonomously perform tasks) to continuously evaluate an organization's security controls, identify gaps, and automate remediation workflows while connecting findings to compliance requirements.

A critical vulnerability (CVE-2026-59726, called RufRoot) in the open-source Ruflo AI agent platform allows unauthenticated attackers to take complete control of enterprise AI systems by exploiting an exposed MCP bridge (Model Context Protocol, a system that lets AI agents interact with external tools and data). The flaw affects Ruflo versions before 3.16.3 and has a maximum severity score of 10.0, enabling attackers to execute arbitrary code, steal API keys, hijack AI agents, and manipulate the platform's memory through a single HTTP request.

Onyx Security raised $113 million in Series B funding to build a platform that helps companies control and monitor AI agents (autonomous software systems that can make decisions and take actions) deployed across their networks. The company's technology uses proprietary AI models to track how AI agents make decisions and stop harmful or unintended behavior in real-time, while also detecting unauthorized AI implementations and protecting against prompt injection attacks (tricking an AI by hiding malicious instructions in its input).

Researchers have found a fundamental flaw in how large language models (LLMs, AI systems trained on massive amounts of text to generate responses) identify who or what is giving them instructions, making them impossible to fully secure against attacks. By exploiting this flaw, the researchers were able to trick popular LLMs into revealing harmful information they were trained not to provide, such as instructions for synthesizing cocaine or sabotaging aircraft navigation systems.

Hidden instructions embedded in Word documents can trick Microsoft 365 Copilot (an AI assistant for Microsoft Office) into modifying data, like changing financial figures, and then copying those hidden instructions into newly generated documents. The researcher who discovered this technique reported it to Microsoft 144 days before publishing, and while Microsoft deployed two mitigations (blocking the original prompt wording and upgrading to GPT-5.5), the vulnerability class remained exploitable even after the updates.

Traditional network firewalls (security tools that filter network traffic based on rules) were designed to inspect where data goes and whether connections should be allowed, but they cannot understand AI-specific activity like prompts (text inputs to AI systems), model calls, or agent-to-agent communication. AI is creating a visibility gap because employees and applications now send requests to AI services across the network, but standard firewalls cannot inspect these interactions for sensitive data exposure or malicious activity. The source introduces an AI Network Firewall that is intent-aware (able to understand the purpose and context behind AI interactions) and integrated into Check Point's AI Defense Plane to detect, inspect, and control AI activity across the enterprise in real time.

This essay argues that whether to use AI depends on distinguishing between 'work' tasks (where only the outcome matters) and 'gym' tasks (where the process of doing the task builds important skills). Writing assignments for students are gym tasks because the struggle of writing, thinking, and revising develops critical thinking skills that atrophy if outsourced to AI, even though AI can produce grammatically perfect essays that lack logical coherence.

Traditional firewalls cannot properly monitor AI-related network traffic because prompts, file uploads, and model calls look like ordinary web traffic, making it impossible to detect if sensitive data is being exposed or if an AI system is being attacked through prompt injection (tricking an AI by hiding malicious instructions in its input). Check Point has created an AI Network Firewall specifically designed to understand and protect these new types of AI connections.