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.

AI-Built Exploit and Sign-In Flaw Opened Path to Internal OpenAI Code

What Happened

Hacktron researchers earned a bug bounty after demonstrating access to OpenAI employee accounts. The post AI-Built Exploit and Sign-In Flaw Opened Path to Internal OpenAI Code appeared first on SecurityWeek .

Why It Matters

According to the report, Hacktron researchers demonstrated a path to access OpenAI employee accounts and earned a bug bounty, with the issue described as involving an AI-built exploit and a sign-in flaw. The article indicates the exposure could have opened access to internal OpenAI code, which makes this primarily a data leakage and account-compromise risk. RealGround analysis: organizations running AI-enabled systems should test authentication flows, session handling, and access controls with red-team methods to reduce the chance that similar flaws expose internal code or other sensitive assets.

Healthcare Fintech SaaS SMB AI startups

RealGround Analysis

This signal maps to data leakage. 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/ai-built-exploit-and-sign-in-flaw-opened-path-to-internal-openai-code/