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CVE-2026-39987: Marimo RCE Exploited to Breach SSH Bastion

CVE-2026-39987: Marimo RCE Exploited to Breach SSH Bastion

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.2 The Hacker News

A skilled human attacker exploited CVE-2026-39987, a pre-authenticated RCE vulnerability in the Marimo notebook platform, pivoting from initial access to an SSH bastion host in just eight seconds using hand-crafted Python tooling. Sysdig's research highlights that expert human operators can match the speed of AI-assisted attacks while demonstrating superior evasion capabilities, bypassing traps that consistently caught every agentic threat actor tested against the same CVE. The incident underscores the ongoing risk posed by interactive, notebook-style AI development environments as high-value attack surfaces in cloud-connected infrastructure.

AGENTIC AIThe Hacker NewsCRITICALCVE-2025-3248: Langflow RCE Enables AutonomousRansomware Attack

CVE-2025-3248: Langflow RCE Enables Autonomous Ransomware Attack

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.8 The Hacker News

Sysdig has documented what it claims is the first end-to-end ransomware attack orchestrated autonomously by an AI agent, attributed to a threat actor tracked as JADEPUFFER. The agent exploited a known remote code execution flaw in Langflow (CVE-2025-3248) to gain initial access, harvest credentials, pivot laterally, and ultimately encrypt and destroy a production database — all without human intervention at the keyboard. The incident demonstrates that AI agents can now lower the skill floor for complex, multi-stage attacks to near zero, representing a qualitative shift in the ransomware threat landscape.

CVE-2026-42208: LiteLLM SQL Injection Stealing AI Credentials

CVE-2026-42208: LiteLLM SQL Injection Stealing AI Credentials

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 BleepingComputer

A critical unauthenticated SQL injection vulnerability (CVE-2026-42208) in LiteLLM, a widely-used LLM proxy and SDK middleware, is being actively exploited to extract API keys, provider credentials, and configuration secrets from the proxy database. Exploitation began within 36 hours of public disclosure, with attackers demonstrating precise targeting of sensitive tables containing OpenAI, Anthropic, and Bedrock credentials. The stolen credentials could enable downstream attacks against AI infrastructure at scale, given LiteLLM's broad adoption across LLM application ecosystems.

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