Topic starter
02/08/2026 11:46 am
TL;DR: A review of 10 real AI security incidents shows attackers are exploiting copilots, agents, MCP servers, and RAG pipelines through prompt injection, authentication bypass, serialization flaws, and command injection, according to Akto. The pattern is clear: AI systems now need identity-aware access control, input trust boundaries, and continuous red teaming, not just model guardrails.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “Top 10 AI Attacks That Shook Enterprises in the Last 12 Months”.
Key questions
Q: What breaks when an AI system cannot separate instructions from data?
A: The trust boundary breaks first, then the policy boundary follows.
Q: Why do agentic AI workflows require stronger identity controls than a single-chatbot model?
A: Agentic workflows can take actions across systems, not just generate text, so they inherit real operational risk.
Q: How do security teams know if an AI workflow is too exposed?
A: Security teams should look for three signals: the assistant can read untrusted free text, it can call tools that touch sensitive systems, and its permissions exceed the narrow task it needs to complete.
Practitioner guidance
- Map AI workflows to explicit trust boundaries Separate untrusted content, retrieved context, model instructions, and tool execution paths so each boundary has its own policy check and logging point.
- Inventory every agent and MCP integration List each AI agent, connector, and MCP server with its associated account, secret, scope, and downstream system access.
- Replace shared or static credentials Use short-lived, task-scoped credentials for AI runtimes and remove hardcoded secrets that let one compromise spread across instances.
Bottom line: AI security incidents are now being driven by attacks on the AI stack itself, not just by using AI as an attacker tool.
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02/08/2026 12:57 pm
Prompt-layer control is now a governance domain, not a model tuning issue: once AI systems ingest untrusted text and then act on it, the security boundary has already moved upstream of the model. The control problem is no longer only output safety, but the integrity of instructions, context, and tool invocation. For identity teams, that means prompt handling must be governed with the same seriousness as privileged application logic.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: Should organisations treat MCP-connected AI systems like privileged workloads?
A: Yes. MCP-connected systems can invoke tools, reach data, and trigger actions, which means they function more like privileged workloads than passive applications. They need least privilege, logging, access review, and clear ownership. If a model can act on behalf of a user or service, its permissions deserve the same scrutiny as any elevated account.
👉 Read our full editorial: Top 10 AI security incidents show why prompt-layer controls matter