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.

Cisco Launches Low-Cost AI Models for Source Code Security

What Happened

The open-weight Antares models are designed to pinpoint known vulnerabilities in codebases faster and at a fraction of the cost of larger AI models. The post Cisco Launches Low-Cost AI Models for Source Code Security appeared first on SecurityWeek .

Why It Matters

Cisco released Antares, a family of open-weight small language models designed to localize known vulnerabilities in source code faster and at much lower cost than larger general-purpose models[1][2][5]. The models are positioned for cybersecurity workflows and are available in open-weight form, with Cisco describing them as intended to help defenders investigate repositories and pinpoint vulnerable files[2][4][6]. RealGround analysis: because these models are meant to be integrated into code-scanning and security pipelines, the main risk is AI supply chain exposure if they are adopted without verification, access controls, and testing for unsafe outputs or workflow misuse.

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/cisco-launches-low-cost-ai-models-for-source-code-security/