But this same flexibility introduces a new class of risks. As agents exchange information and trigger actions across interconnected systems, vulnerabilities can arise from the tools and servers they depend on, including poisoned or untrusted MCP servers, unsafe command chains, and toxic flows that lead to data leaks or privilege escalations. A single malicious or misconfigured component can compromise the entire workflow.
That’s why MCP scanning is becoming a critical guardrail for secure development. Much like code and container scanning became standard in modern pipelines, organizations adopting agentic workflows need visibility into how agents connect, share, and act. Without it, AI systems risk introducing vulnerabilities at the very layer designed to make them most useful.
MCP 101: What is MCP?
MCP extends large language models beyond their training data by connecting them to external tools, databases, APIs, and file systems. With MCP, an agent can execute real actions, query live data, trigger scripts, or update systems rather than just generating text.
This makes powerful workflows possible: an agent could pull customer records, analyze them, and update a CRM automatically, or resolve engineering issues by fetching logs and running scripts.
But every new connection point also creates risk. A misconfigured API, unsafe file access, or careless handoff between agents can expose sensitive data or escalate privileges. MCP unlocks enormous potential but also expands the attack surface in ways that demand new security guardrails.
The toxic flow problem
When a single agent links multiple tools and data sources through MCP, the primary risk comes from how those connections interact. Unsafe configurations or chaining patterns can create toxic flow situations where tool commands, permissions, or data paths combine in unintended ways, opening new opportunities for attack or data exposure.
For example, an agent might invoke a tool that passes unvalidated input into another process, creating an unsafe command chain. Alternatively, it may connect to a malicious or untrusted MCP server that delivers poisoned tool manifests, causing the agent to execute harmful or unauthorized actions. In both cases, the vulnerability doesn’t stem from a single tool, but from how the agent uses its connected resources.
These risks are part of why MCP scanning is important. It inspects the relationships between tools and servers to identify unsafe chains, detect poisoned or vulnerable components, and prevent toxic flows before they cause real damage.
Scanning and guardrails for MCP
The first step toward securing agentic AI is knowing what’s running in your environment. MCP scanning provides security teams with visibility into the agent-based tools installed on developer machines, including which MCP servers and tools are present, where they originated, and how they’re configured.
Today, MCP servers can be installed locally through tools like Cursor or Windsurf, often without centralized oversight. With thousands of publicly available MCP servers, many poorly implemented or even malicious security teams are left in the dark about what developers are connecting to. MCP scanning closes that gap by providing an inventory of active MCP servers and tools, helping teams understand their exposure and assess potential risk.
Beyond visibility, MCP scanning also performs lightweight assessments of these components, detecting vulnerabilities or insecure configurations in installed MCP servers and tools. This early insight helps organizations address risk before it becomes a pathway for attack.
While runtime enforcement and behavioral guardrails remain an area of active research, today’s focus is on visibility and assessment, the foundation for securing agentic AI at the endpoint. By starting here, organizations gain practical insight into how MCP-based tools are being used and where action is needed to protect developer environments.
The role of AI-BOM in MCP security
While MCP scanning helps identify risks in how agents connect and operate, security teams also need visibility into what those agents are built on. Here’s where an AI Bill of Materials (AI-BOM) can help. Much like a traditional software bill of materials catalogs application dependencies, an AI-BOM inventories the datasets, models, frameworks, and MCP Servers in your environment.
Together, AI-BOM and MCP scanning provide full visibility across two critical layers:
Applications: The AI-BOM maps every component within an organization’s AI systems, giving security teams a clear view of assets and dependencies.
Machines: MCP scanning extends that visibility to development endpoints, validating that the agentic tools running there are safe and properly configured.
This unified view connects what’s in your AI stack with where it runs.
By linking AI-BOM insights with MCP scanning, organizations gain continuous oversight from development to deployment. The result is a holistic layer of protection that secures both the AI infrastructure and the endpoints where agents operate.
Why this matters for endpoint security
Endpoints are where agentic tools take real action, reading files, executing commands, and interacting with live systems. Without visibility at this layer, even well-secured AI workflows can hide unsafe behavior or unverified connections.
MCP scanning brings that activity into view. By showing security teams which MCP servers and tools are running locally, it exposes potential blind spots before they become problems. This visibility transforms endpoints from unknown risk zones into manageable components of the security landscape, enabling organizations to build safer AI practices without slowing down development.
Turning blind spots into secure workflows
Agentic AI workflows redefine how software is built and operated, but also introduce new blind spots through MCP connections and the tools agents rely on. Left unchecked, these connections can erode trust in the very systems designed to accelerate innovation.
That’s why MCP scanning and AI-BOM visibility are valuable guardrails that make safe AI adoption possible. Together, they allow teams to see what their agents are built on, understand how those components behave in practice, and stop insecure flows before they spread.
Snyk is leading the way by directly bringing these capabilities to real-world environments, integrating them into endpoint and application security workflows and turning emerging research into practical tools for development and security teams.
Want to explore agentic security? Try Snyk MCP-Scan today.
