It seems that the AI agent security industry may be repeating familiar mistakes: reaching for detection as a first-line preventative control instead of doing the structural work.

Detection is not prevention. A filter that can be probed and evaded by the system it is protecting is not a control. It is a delay.

Instead, treating security as an engineering problem leads to invariants: what can we make structurally impossible? What attack surface can we completely eliminate? Detection comes after, augmenting a foundation that does not depend on it.

For AI agents, the structural question is: can we constrain the agent to a path aligned with human intent, rather than trying to detect whether it behaves maliciously?

More below:
https://securityblueprints.io/posts/agent-perimeter-fallacy/

#AIAgentSecurity #OpenSource #Cybersecurity #AIGovernance #LLMSecurity

OpenClaw breaches exposed 42,665 AI agents 93.4% vulnerable to prompt injection attacks that steal API keys and private data. AdwaitX reveals OWASP's #1 LLM threat and defense strategies every developer needs in 2026 #AdwaitX #AIAgentSecurity #PromptInjection

https://www.adwaitx.com/openclaw-prompt-injection-ai-security-defense/

An impending update to #ModelContextProtocol marks an important step toward secure, personalized #AI, but also shows that significant work remains to secure #AIagents.

My writeup, featuring an exclusive interview with Alex Salazar, whose company authored the contribution, and reaction from IT pros about the significance of the change: https://www.techtarget.com/searchsoftwarequality/news/366634681/MCP-OAuth-update-adds-security-for-personalized-AI #MCP #AIgovernance #AIsecurity #AIagentsecurity #OAuth