If your team is running AI agents in production — or planning to — the security conversation can no longer be deferred. The OWASP Agentic AI Top 10 and Bright Security’s companion MCP AppSec playbook, both published this week, give security and engineering teams the most complete picture yet of what can go wrong when you hand autonomous agents real credentials and real access.
This isn’t theoretical. These are attack patterns being actively exploited in early production deployments right now.
Why Agentic AI Security Is Different
Traditional application security is about protecting resources from untrusted users. Agentic AI security adds a new and deeply uncomfortable dimension: you now have to protect resources from your own agents, which can be manipulated into acting against your interests by malicious content in the environment.
The attack surface is unlike anything AppSec teams have had to reason about before. An agent that reads emails, accesses databases, and calls external APIs — and that uses an LLM to decide what to do next — can be steered by content injected into any of those inputs.
The OWASP Agentic AI Top 10
The OWASP Agentic AI Top 10 covers the attack categories teams need to defend against. The most critical for practitioners:
1. Indirect Prompt Injection
An attacker embeds malicious instructions in content that an agent will read — a web page, a document, an email, a database record. The agent’s LLM interprets these as legitimate instructions and executes them. Unlike direct prompt injection (which requires access to the chat interface), indirect injection can be triggered by any content source the agent touches.
Example: An agent tasked with summarizing emails reads a phishing email that contains hidden instructions to forward all future emails to an external address. The agent complies.
Mitigation: Strict separation of instruction sources (trusted) and data sources (untrusted). Never let content from data sources override system-level instructions. Consider sandboxed summarization for untrusted inputs.
2. Over-Permissioned Agent Identity
Agents are frequently given broad access because it’s easier than scoping permissions precisely. An agent that needs to read two Confluence spaces gets read access to all of Confluence. An agent that needs to post to one Slack channel gets a bot token that can post anywhere.
When such an agent is compromised — through prompt injection or a model jailbreak — the blast radius is proportional to those permissions.
Mitigation: Principle of least privilege, applied rigorously. Scope agent credentials to exactly what they need. Prefer short-lived tokens over persistent credentials. Use separate identities for different agents.
3. MCP Server Exploitation
The Model Context Protocol (MCP) has become a standard way to give agents access to tools and data sources. But an MCP server is also an attack surface: a malicious or compromised MCP server can feed poisoned tool outputs back to an agent, manipulate tool call parameters, or exfiltrate data through tool response channels.
Mitigation: Vet and pin MCP server versions. Don’t trust tool outputs as authoritative without validation. Monitor MCP traffic for anomalies.
4. Supply Chain Attacks on Agent Components
Agents depend on LLM providers, tool libraries, MCP servers, and memory backends. Any of these can be compromised at the supply chain level — malicious updates to a popular MCP server package, a poisoned model checkpoint, a dependency with a backdoor.
Mitigation: Pin dependencies. Verify checksums. Use private registries for production agents. Monitor for supply chain alerts.
5. Credential Theft Through Tool Calls
Agents that carry credentials (API keys, OAuth tokens, service account creds) can be manipulated into exfiltrating those credentials through seemingly legitimate tool calls. An agent with database access can be prompted to “explain the connection string used in the last query” — and comply.
Mitigation: Store credentials in secrets managers, not in agent context. Redact credentials from agent-visible logs and memory. Use credential injection at runtime rather than embedding in prompts.
Bright Security’s MCP AppSec Checklist
Bright Security’s companion piece adds a practical checklist specifically for MCP-integrated workflows. The key checks:
- All MCP servers are on an approved, reviewed list — no ad-hoc additions
- MCP traffic is logged and monitored at the network layer
- Each agent has an explicit, minimal permission set
- Human approval gates exist for any action that modifies data or sends external communications
- Agent session logs are retained and reviewable
- Indirect prompt injection test cases are part of your agent’s QA suite
- Credential exposure is tested before any agent goes to production
The Bottom Line
The OWASP Agentic AI Top 10 and MCP AppSec playbook together represent the current state of the art in agentic security thinking. They’re not perfect — this field is moving too fast for any framework to be comprehensive — but they’re the most systematic starting point available.
If you’re running production agents today without a structured threat model that covers indirect prompt injection, over-permissioned identities, and MCP server trust, you have work to do. These documents are where to start.
Sources
- OWASP Agentic AI Top 10 — DEV Community
- OWASP MCP Top 10 Project
- MCP Security in 2026: AI Agent Integrations Need Their Own AppSec Playbook — Bright Security
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260320-0800
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