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.

Microsoft Azure DevOps MCP Flaw Lets Hidden PR Comments Hijack AI Review Agents

What Happened

A single invisible comment in an Azure DevOps pull request can turn a reviewer's own AI coding agent against them, driving it into projects the attacker has no rights to reach and quietly leaking what it finds. The flaw is in Microsoft's official Azure DevOps MCP server, and it works because one of its tools returns pull request descriptions without a prompt-injection guardrail the company had

Why It Matters

The reported issue says a hidden pull request comment in Azure DevOps can influence an AI review agent through Microsoft’s Azure DevOps MCP server, causing the agent to act outside the intended review scope and potentially leak information it discovers. The article also says there was no fixed release or public CVE at the time, and that the weakness stems from one MCP tool returning pull request descriptions without prompt-injection guardrails. RealGround analysis: this is best classified as indirect prompt injection with AI agent abuse potential, so controls should focus on tool-authorization boundaries, least-privilege scoping, and red-teaming of agent workflows that ingest untrusted PR content.

Healthcare Fintech SaaS SMB AI startups

RealGround Analysis

This signal maps to indirect prompt injection. 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://thehackernews.com/2026/07/microsoft-azure-devops-mcp-flaw-lets.html