What breaks is the chain of custody around intent, state, and result. The agent has to guess syntax, preserve intermediate outputs, and reconcile multiple calls, which creates more room for errors and overreach. In security and observability workflows, that usually means slower investigations and weaker auditability.
Why This Matters for Security Teams
Freeform tools turn an investigation into a conversational exercise, but investigation work is not conversational by default. It depends on repeatable steps, bounded actions, and defensible outputs. When an AI agent can improvise tool use, security teams lose the ability to predict which data it will touch, which commands it will chain, and how it will explain its own conclusion. That undermines auditability, slows incident response, and raises the chance of overcollection or accidental disclosure of secrets.
This is why guidance in the OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework increasingly emphasizes bounded execution, governance, and traceability instead of raw flexibility. NHIMG research on the OWASP NHI Top 10 also shows that agentic systems fail in the gaps between identity, intent, and tool access, not just at the credential layer. The practical problem is not only that an agent may choose the wrong tool, but that freeform tools make it difficult to prove what the agent intended, did, and observed at each step.
NHIMG data shows how fast this risk becomes operational: in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research, exposed AWS credentials were attempted within an average of 17 minutes. In practice, many security teams encounter the weakness only after an agent has already retrieved too much, too broadly, or too noisily to reconstruct cleanly.
How It Works in Practice
Freeform tools usually fail because they give the agent broad syntactic freedom without enough structural guardrails. In an investigation workflow, that means the agent may generate ad hoc queries, retry with slightly different prompts, stitch together partial outputs, and then summarize the result as if the path were deterministic. The issue is not simply accuracy. It is that the investigation chain becomes hard to replay, hard to validate, and hard to attribute to a specific policy decision.
Safer patterns are emerging, but current guidance suggests treating tool use as an orchestrated workflow rather than an open-ended prompt loop. Teams are moving toward:
- Strictly typed tools with constrained parameters, so the agent cannot invent arbitrary actions.
- Policy-as-code checks at request time, aligned with CSA MAESTRO agentic AI threat modeling framework and real-time control evaluation.
- Workload identity and short-lived access tokens, so the agent proves what it is before every sensitive action.
- Step-level logging that captures intent, input, output, and disposition for each tool invocation.
That design is closer to how NHI governance should work for autonomous systems: the agent should not carry broad standing access just because it may need it someday. It should receive just-in-time privileges, operate within a narrow task boundary, and revoke access automatically when the task ends. NHIMG analysis in the Analysis of Claude Code Security shows that freeform capability often improves speed at the expense of control, especially where investigative workflows touch secrets, logs, or production systems. These controls tend to break down when the agent is allowed to chain tools across multiple systems because the runtime context and the audit trail diverge too quickly.
Common Variations and Edge Cases
Tighter tool control often increases friction, requiring organisations to balance investigation speed against reproducibility and containment. That tradeoff is real: analysts may want freeform access during active incidents, but the same openness can create unbounded blast radius if the agent misclassifies a resource, over-queries a dataset, or follows an unsafe branch.
There is no universal standard for this yet. In highly regulated environments, the best practice is evolving toward curated tool catalogs, approval gates for sensitive actions, and separate modes for exploratory versus evidentiary work. In less mature environments, teams often start by restricting write actions and external network access before they tackle read-only investigations. The important point is that freeform tools are not the same as flexible operations; flexibility without constraint is just ambiguity.
Edge cases matter. A sandboxed dev environment can tolerate broader exploration than a live SOC case management system. A single-agent assistant may be easier to bound than a multi-agent pipeline where one agent gathers evidence and another drafts remediation steps. For those scenarios, the State of Secrets in AppSec research is a useful reminder that weak handling of sensitive inputs compounds quickly: 43% of security professionals are already concerned about AI systems learning and reproducing sensitive information patterns from codebases. Freeform investigation tools make that concern operational, because they increase the chance that sensitive context is copied, summarized, or retained outside the intended boundary.
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 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | A07 | Freeform tools increase agentic misuse and unsafe tool chaining. |
| CSA MAESTRO | TMM-03 | MAESTRO addresses threat modeling for agent workflows and tool exposure. |
| NIST AI RMF | AI RMF governance applies to traceability, accountability, and safe operation. |
Model each investigation step, then restrict tools and data paths that expand blast radius.
