What Happened
This post summarizes several agentic AI security issues, including prompt injection in GitHub’s Agentic Workflows and the GitHub MCP server that could leak private repository data. It also discusses Salesforce Agentforce prompt injection and other incidents involving untrusted content influencing agent behavior.
Why It Matters
The article reports multiple agentic AI security issues, including prompt injection vulnerabilities in GitHub’s Agentic Workflows and MCP server that could expose private repository data, as well as prompt injection risks in Salesforce Agentforce and other scenarios where untrusted content steers agent behavior. These are documented incidents of agents ingesting and acting on malicious or untrusted prompts, leading to potential data leakage and unintended actions. From a RealGround perspective, this highlights the need for systematic prompt injection defenses, strong input validation and isolation, and rigorous testing of agent tool-use paths before production deployment. Organizations should combine secure agent design, business logic audits, and ongoing red teaming to detect and mitigate these prompt-driven attack surfaces in agentic AI systems.
RealGround Analysis
This signal maps to 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://curity.io/blog/5-agentic-ai-security-incidents-and-vulnerabilities/