The Tool Desk
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Why an agent’s capabilities shape its risk
An AI agent may read emails, search files, call APIs, or make changes through connected tools. Those capabilities create an attack surface: if untrusted content can influence the agent, the consequences depend partly on what the agent is authorized and technically able to reach.
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NIST’s Center for AI Standards and Innovation (CAISI) describes agent hijacking as indirect prompt injection: an attacker places malicious instructions in data an agent may ingest, such as an email, file, or website, in an effort to make it take unintended harmful actions. The risk is not limited to a user typing a malicious prompt directly.
OWASP’s guidance on excessive agency identifies excessive functionality, permissions, and autonomy as common root causes. This explains the value of pre-runtime controls: they constrain the agent’s available tools, credentials, and reachable resources before a particular instruction is processed. Detection observes behavior and can help teams respond, but it does not itself make an unauthorized action impossible.
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Pre-runtime controls and runtime detection do different jobs
| Control layer | What it does | What it cannot establish by itself |
|---|---|---|
| Pre-runtime capability and authorization controls | Limit available tools, operations, credentials, destinations, and permissions before invocation; enforce whether a proposed action is allowed. | That every permitted action is safe, or that prompt injection has been eliminated. |
| Runtime monitoring and detection | Observe agent and downstream activity, surface suspicious behavior, support rate limits, and inform incident response. | That an action will be blocked before it occurs, or that a clean final response means no side effect happened. |
The comparison is about where a control acts, not a measured contest with a universal winner. OWASP notes that monitoring and rate limits can help limit damage and improve discovery, while they do not prevent excessive agency. Use both layers: prevent actions that should never be possible, and monitor for misuse, mistakes, and gaps in the preventive controls.
Build the boundary outside the model
Reduce tools and permissions before invocation
Inventory the tools, connectors, data sources, identities, and network destinations available to each agent. Remove anything the task does not require. Where a generic shell, fetch, or write capability can be replaced by a narrow task-specific operation, prefer the narrower operation. OWASP recommends limiting both the number of tools and their functionality, and enforcing minimum permissions in downstream systems.
Give each agent a distinct identity rather than letting agents share a broad user or service credential. Google Cloud recommends dedicated agent identity and least-privilege roles. Grant only the downstream roles and scopes required for that agent’s task, and separate user or tenant data and memory so one context cannot casually inherit another’s access.
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Authorize the exact action in the execution path
The model can propose an action; a trusted execution component should independently decide whether it is authorized. OWASP’s Excessive Agency guidance states: “Implement authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not.” Treat model-generated reasoning or a claim that an action is safe as input, not as the access-control decision.
For consequential operations, bind approval to the actual actor, tool, target, and normalized parameters. A user approving “send the message” should see the recipient and content that will actually be sent, not a general request for permission. Validate authorization and approval independently of model output, and fail closed if that validation cannot be completed. For irreversible actions, short-lived approval artifacts and replay protection reduce the chance that approval can be reused for a different or later action.
Contain the execution environment
Restrict the agent’s filesystem, process environment, and network access to what the task needs. Sandboxes or virtual machines can limit reachable resources; egress restrictions can narrow where data can go. Treat retrieved documents and tool outputs as untrusted content. Labels or delimiters may help organize prompts, but OWASP warns that labeling alone does not create an enforcement boundary.
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Anthropic’s 2026 description of containment for Claude products says credentials excluded from a sandbox cannot be exfiltrated from that sandbox. That is a vendor’s account of its engineering approach, not independent comparative evidence that sandboxing defeats every attack. A sandbox still needs to be configured around the task’s real inputs, outputs, credentials, and network requirements.
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Log agent actions and downstream effects, not only the final text shown to a user. Useful telemetry can include the invoked tool, target, arguments, authorization decision, approval state, and resulting change, subject to the organization’s privacy and retention requirements. Rate limits and alerts can help surface unusual activity or constrain repeated attempts.
Plan what responders can do when an alert fires: revoke or suspend the agent identity, disable a connector, stop queued work, and investigate downstream systems for completed actions. A refusal or harmless-looking final answer is not proof that the agent did not already call a tool or alter data. Detection is most useful when teams can connect observed activity to a concrete containment and recovery path.
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Evaluate tool calls and side effects, not just answers
Test direct prompt injection as well as indirect attacks embedded in harmless-looking emails, files, web pages, and tool outputs. Use non-production data and instrumented tool substitutes so tests can show whether an action would have occurred without causing real harm. Measure attempted and successful tool calls, authorization outcomes, and side effects—not only whether the final answer appears safe.
NIST CAISI’s “Strengthening AI Agent Hijacking Evaluations,” published January 17, 2025 and updated December 19, 2025, describes tests of Claude 3.5 Sonnet, released in October 2024, in AgentDojo environments covering workspace, travel, Slack, and banking tasks. CAISI added database-exfiltration and automated-phishing scenarios and reported that agents were frequently induced to follow malicious instructions across three new risk areas. The report does not provide a universal success rate for agents generally.
CAISI also reported that attacks newly developed for the upgraded model substantially increased measured attack success relative to attacks tested previously. That finding makes fixed test sets a weak basis for confidence: vary the attack, task, and surrounding content, and examine multiple attempts. OWASP’s prompt-injection smoke-test page lists 14 hand-picked attack inputs and seven benign requests, but explicitly says these examples are a smoke test, not a representative security benchmark.
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How to compare agent security designs
When reviewing an architecture or vendor design, ask for evidence against the same task and threat model. These are decision axes from the cited guidance, not scores from a comparative product test:
- Reach: Which tools, operations, downstream permissions, data stores, and network destinations can the agent access?
- Isolation: How are filesystem, memory, credentials, and network access bounded?
- Independent enforcement: Does a component outside the model validate authorization and approval before executing an action?
- Approval quality: Does approval identify the exact actor, tool, target, and parameters, and is it protected against reuse?
- Observability and containment: Can operators see downstream effects and quickly suspend access or stop work?
- Evaluation quality: Do tests adapt attacks, cover task-specific risks, and measure real tool calls and side effects over multiple attempts?
What published figures do—and do not—show
Anthropic reported in 2026 an 84% reduction in permission prompts after adding OS-level sandboxing to the described Claude Code setup. That is a product-experience figure about permission prompts, not an independent measure of security effectiveness.
Anthropic also reported roughly 0.1% attack success on single attempts and around 5–6% after 100 adaptive attempts on Gray Swan’s Agent Red Teaming benchmark for Claude Opus 4.7. Those figures are specific to the named model and benchmark and are vendor-reported; they are not a general guarantee about agent security or a controlled comparison of pre-runtime controls with runtime detection.
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