Attackers now move at machine speed, forcing security teams to build and maintain a real-time context of their cloud, workload, and AI model environments before any alert fires. Teams must automate continuous inventory and connect signals across these layers so AI agents can investigate and respond at machine speed. This shifts SecOps from reactive investigations to proactive context-driven defense.

A US official told AP that Anthropic’s Mythos model identified vulnerabilities in classified government systems within hours during Project Glasswing tests with intelligence agencies. The Trump administration then barred foreign access to Mythos and its limited Fable 5 model under a security directive, prompting cybersecurity experts to warn that restricting these tools could weaken US defenses.

Four high-severity flaws in the open-source Dify platform allow authenticated users to read private chats, preview documents, and leak files across tenants by abusing tracing endpoints, plugin daemon APIs, and flawed file permissions. One issue also stems from a vulnerable PDFium version (CVE-2024-5846). Dify 1.14.2 patches these bugs; operators should update immediately and apply WAF rules for CVE-2026-41948.

IBM, Red Hat and Palo Alto Networks are integrating Palo Alto’s network-based virtual patching in Prisma with IBM/Red Hat’s Project Lightwell to spot and shield against open-source software flaws. The joint effort uses shared vulnerability intelligence and AI-driven processes to deliver preemptive, same-day network protections while patches are developed.

This daily digest covers a mass credential harvest via FortiBleed targeting FortiGate firewalls, new backdoors like ModeloRAT and Mistic tied to ransomware brokers, and critical data-exposure flaws in platforms such as Dify AI. It also highlights supply-chain risks in open-source CI/CD workflows, Anthropic’s Mythos model uncovering classified-system weaknesses, and industry moves on AI-driven SecOps and network-layer virtual patching.

This issue covers Databricks’ planned acquisition of Panther for AI-powered SOC threat detection and AWS’s new Continuum service that automates vulnerability discovery and fixes at machine speed. It also highlights Vercel’s secure data access tokens, Cisco and NVIDIA’s AI networking push, Iru’s config-as-code device management, Snowflake’s support for the Agentic Resource Discovery spec, and best practices against supply-chain attacks.

Databricks is buying Panther to bolster its security lakehouse platform as an alternative to legacy SIEM systems. Panther’s AI-driven SOC tools and over 100 pre-built integrations will automate threat detection, alert investigation, and response at scale.

A Jamf survey shows more than 20% of organizations running macOS networks have lost money or been breached due to AI tool use, and about 60% expect future incidents. Shadow AI, agentic AI, vendor sprawl and usage-based billing are creating governance blind spots. Jamf urges early governance measures: regular audits, strict data-access policies and use of built-in tools.

This issue covers fresh attacks on AI agent infrastructure—over 7,000 Langflow servers hit via chained bugs in LangGraph and LangChain—and a new agentjacking risk where exposed Sentry keys let attackers hijack Claude-based workflows. It also details Apple’s Beats Studio Buds wiretap patch, Gizmodo’s ClickFix malware incident, and ongoing FortiBleed fallout, plus guidance on client-side bot detection, post-quantum crypto, and microVM limits.

Researchers at Tenet Security showed how anyone with a public Sentry DSN can inject a fake error report that coding agents like Claude Code, Cursor, and Codex will treat as a fix instruction. The agent fetches the malicious payload via the Model Context Protocol and runs arbitrary commands on the developer’s machine, exposing environment secrets and credentials. Sentry won’t close the write endpoint, leaving the fix to agent runtimes to filter untrusted data.

Gray Swan cofounders Zico Kolter and Matt Fredrikson explain why AI systems need a different security mindset, focusing on indirect prompt injection, agent vulnerabilities and correlated failures. They walk through automated red teaming tools like Shade and the Gray Swan Arena, discuss guardrails, and argue that bigger models aren’t inherently safer and require bespoke security, identity management, and compliance measures.

This roundup covers the Tata Electronics data breach exposing Apple and Tesla secrets, a critical FFmpeg RCE patch, and Meta’s halted keystroke-tracking AI program. It also reviews Linux AF_ALG privilege escalation mitigation, new prompt-injection tactics against LLMs, OpenClaw skill-market threats, and OpenAI’s Daybreak security tools alongside warnings of near-term AI-driven cyberattacks.

Zscaler unveiled a zero trust platform to secure autonomous AI agents’ data access, communications and device activity. It adds an AI Broker for agent-to-agent and data calls, endpoint AI threat detection, an AI Access Graph for mapping identities and data flows, and expanded AI Protect controls. This aims to give each AI agent its own identity, permissions and real-time monitoring.

This digest covers new exploits in AI and enterprise platforms, including a path traversal flaw in Langflow, a ServiceNow tenant data leak, and critical Ivanti Sentry root bugs. It also highlights Anthropic’s ATT&CK mapping of AI-driven threats and evolving deepfake tactics for bypassing facial recognition.

Anthropic’s CISO reveals that Claude AI generates 90% of their code and walks through their secret-protection measures. He highlights how plain .env files can expose sensitive data in AI workflows and shares a detailed security configuration.

A private online forum obtained Mythos the day Anthropic began limited company testing. According to a source with screenshots and a live demo, the group has kept using the model regularly without permission.

Security researchers found that Anthropic’s new Mythos AI model was reachable by unauthorized users through exposed API endpoints. This lapse could expose sensitive prompts and responses, prompting Anthropic to investigate and strengthen its access controls.

Quodeq is an MIT-licensed tool that runs locally to scan codebases using AI across six ISO 25010 dimensions, mapping each finding to CWE identifiers and providing fix plans. It supports cloud and local models, outputs grades and violations in JSON, and includes a dashboard for exploring results and defining custom standards.

The UK’s AI Safety Institute tested Claude Mythos and found its ability to uncover security flaws scales directly with the number of tokens spent. This creates a simple economic model: defenders must outspend attackers on AI-driven reviews to stay secure. It also boosts the value of open source libraries, since multiple users can share the cost of token-based audits.