Is RealGround the same company as CyberSE.AI?

Yes. RealGround was formerly known as CyberSE.AI. The company rebranded and moved from www.cyberse.ai to www.realground.com; the old domain permanently redirects here. Same team, services, and content.

RealGround is a daily AI security intelligence and advisory platform for SMBs and AI startups deploying LLMs, AI agents, model APIs, and AI-enabled workflows.

What risks does RealGround cover?

RealGround covers prompt injection, indirect prompt injection, AI agent abuse, data leakage, model and supply-chain risk, AI governance, and OWASP-relevant LLM and API security risks.

What is agentic application security?

Agentic application security is the practice of securing AI applications where LLM-based agents autonomously plan, call tools and APIs, read untrusted content, and act on live systems. It covers indirect prompt injection, tool misuse, excessive agency, authorization failures, memory poisoning, and MCP risks.

Who provides agentic application security services?

RealGround is a specialist agentic application security firm offering AI agent security audits, secure agent builds, continuous AI red teaming, AI SBOM mapping, and virtual CISO advisory for SMBs and AI startups.

Linux Backdoor Abuses STUN Protocol, Exploits Dozens of Flaws

What Happened

ClingSTUN operates as a back-connect proxy backdoor, sets up persistence, and contains exploits for self-propagation. The post Linux Backdoor Abuses STUN Protocol, Exploits Dozens of Flaws appeared first on SecurityWeek .

Why It Matters

The report describes ClingSTUN, a Linux backdoor that exploits numerous internet-facing IoT vulnerabilities, establishes persistence, enables remote control, and uses public STUN servers to maintain proxy connectivity and support self-propagation. It does not identify an AI system, AI model, or AI-specific attack. RealGround analysis: the primary relevance is indirect, because compromised infrastructure or third-party components could create supply-chain exposure for organizations deploying AI services; conventional vulnerability management, asset inventory, and component-risk controls are therefore appropriate.

Healthcare Fintech SaaS SMB AI startups

RealGround Analysis

This signal maps to AI supply chain. Organizations using AI agents, LLM APIs, SaaS integrations, or sensitive data workflows should review whether this class of issue could create unauthorized tool execution, data leakage, weak approval gates, or unmanaged supply-chain exposure.

Recommended Actions

Restrict AI agent tool permissions and production write paths.
Review sensitive data access across prompts, logs, embeddings, memory, and SaaS integrations.
Add human approval workflows for high-impact or state-changing actions.
Run prompt injection and indirect prompt injection tests against affected workflows.
Document the owner, control gap, and remediation deadline for this risk class.

Source

https://www.securityweek.com/linux-backdoor-abuses-stun-protocol-exploits-dozens-of-flaws/