The owning organisation remains accountable for the agent outcome, even if the tool server is managed elsewhere. Third-party control does not remove the need for internal approval, monitoring, and revocation logic. Shared responsibility only works when the runtime behaviour is observable and governable.
Why This Matters for Security Teams
When a third-party MCP tool changes its description, the risk is not the text itself but the control boundary it may signal. In agentic workflows, tool descriptions can influence what an AI agent calls, what data it assumes is safe to share, and whether the runtime still matches the original approval decision. OWASP’s OWASP Agentic AI Top 10 and NHIMG’s research on OWASP Agentic Applications Top 10 both point to the same operational issue: autonomous systems are sensitive to runtime context, not just static registration data.
That is why accountability stays with the owning organisation, even when the MCP server is operated by a vendor or partner. A changed description can be a benign documentation update, or it can be a sign that the tool’s intent, scope, or side effects have changed. Security teams must treat that as a governance event, not a cosmetic one. In practice, many security teams encounter tool drift only after an agent has already used the changed capability in production.
How It Works in Practice
Accountability works best when the organisation that authorises the agent also controls the policy gate that approves tool use. That means the third-party provider may own the infrastructure, but the consuming organisation still owns the decision to allow the agent to invoke the tool, under what conditions, and with what limits. For MCP-backed agents, current guidance suggests treating tool descriptions as part of the security contract, not just metadata. If the description changes, the trust decision should be re-evaluated before the agent continues using it.
Operationally, that usually means:
- Registering approved MCP tools with explicit owner, purpose, and risk classification.
- Monitoring for description, scope, schema, or permission changes as change events.
- Re-running policy checks when a tool changes, rather than relying on the original approval.
- Using short-lived credentials and revocation paths so access can be withdrawn quickly.
- Keeping an audit trail that ties agent actions back to the exact tool version in use.
This approach aligns with the OWASP Non-Human Identity Top 10, because the issue is not only access, but the lifecycle of non-human identities and their dependent secrets. NHIMG’s State of MCP Server Security 2025 found that only 18% of MCP server deployments implement any form of access scoping for tool permissions, which shows how often governance is weaker than the integrations it supports. The practical answer is to pair tool approval with continuous runtime observation, so a changed description triggers review before the agent can chain that change into broader access. These controls tend to break down when the tool is dynamically generated or self-updating, because the approved version and the live version diverge too quickly for manual review.
Common Variations and Edge Cases
Tighter MCP control often increases operational overhead, requiring organisations to balance agility against assurance. There is no universal standard for this yet, especially where vendors expose fast-moving agent tool catalogs or allow descriptions to be edited without a formal release process. In those environments, best practice is evolving toward intent-based approval: the organisation approves what the tool is allowed to do, not just the vendor URL it came from.
Two edge cases matter most. First, if the tool description changes but the schema and permissions do not, the update may still be safe, but it should be logged and reviewed because agent behaviour can shift from wording alone. Second, if the description change accompanies a scope change, the organisation should treat it as a new tool until re-approved. This is especially important for shared MCP endpoints, where multiple agents may inherit the same integration path. For context on how quickly agentic systems can drift beyond intended use, NHIMG’s AI Agents: The New Attack Surface report shows that 80% of organisations have already seen agents act beyond intended scope. In short, third-party operation does not transfer accountability; it only shifts where the technical evidence must be collected and enforced.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A1 | Agent tool drift is a core agentic app risk. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Tool access depends on managed NHI credentials and revocation. |
| CSA MAESTRO | TRUST-02 | Third-party tool changes require runtime trust reevaluation. |
| NIST AI RMF | Accountability and monitoring for AI actions fit AIRMF governance. | |
| NIST Zero Trust (SP 800-207) | PA-3 | Dynamic tool changes need policy enforcement at request time. |
Bind tool approval to continuous trust checks, not one-time onboarding.
Related resources from NHI Mgmt Group
- Who is accountable when sensitive data is retained in a third-party AI tool?
- Who is accountable when a third-party vendor tool introduces risk into CUI systems?
- Who is accountable when third-party credentials remain active after a healthcare relationship changes?
- Who is accountable when a trusted third-party tool is abused for lateral movement?
