Security for AI: GenAI Risks and the Emerging Startup Landscape

Menlo Ventures outlines concrete security risks in production LLM and agent deployments, including prompt injection, insecure output handling, sensitive information disclosure, insecure plugin design, model theft, and data poisoning attacks.[1] The piece highlights demonstrations such as synthetic ChatGPT compromises at Black Hat and a poisoned GPT-J-6B model on Hugging Face, emphasizing that AI models and their supply chains are becoming primary targets for attackers and a focus area for new security startups.[1]

The Menlo Ventures article describes multiple concrete risks across the AI lifecycle, including prompt injection, insecure output handling, sensitive data disclosure, insecure plugin design, model theft via compromised credentials or supply chain attacks, and data poisoning of open-source models (e.g., a poisoned GPT-J-6B on Hugging Face that went unnoticed before disclosure).[1] It emphasizes that AI models and their surrounding ecosystem—foundational models, plugins, code, datasets, and hosting platforms—are now primary targets for attackers, making the AI supply chain a critical focus for emerging security startups.[1] From a CyberSE.AI perspective, these findings imply organizations must treat models, datasets, plugins, and third-party AI services as a unified supply chain that requires SBOM-style asset inventory, provenance tracking, and continuous integrity monitoring. Systematic AI supply chain governance and hardening can materially reduce the risk of model theft and poisoning propagating into production systems, and should be integrated with broader security controls for agents, plugins, and data flows.