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A sandbox can limit what code an AI agent can do in a particular environment, but it cannot decide whether the agent is authorized to take an action, prevent malicious instructions hidden in an email or webpage from influencing it, or ensure that the agent cannot access sensitive data and credentials. Secure agents with multiple independent controls: narrow permissions, protected data and network paths, safeguards around consequential actions, and monitoring and adversarial testing. Treat the model as a component that can be influenced—not as the security boundary.
Why isn’t a sandbox enough?
A sandbox controls some effects of execution. An agent’s risks also arise from the authority it has, the tools and information it can reach, the instructions it encounters, and the actions it can commit. An agent may stay inside its execution environment and still misuse an over-broad API, disclose information through an allowed connection, or act on malicious text in a document it was asked to read.
OWASP’s agent-security guidance treats these as distinct risk and control areas, including authorization, tool access, sensitive data, memory, network paths, delegated agents, approvals, and auditability. The practical implication is that containment must extend beyond code execution: enforce policy at trusted boundaries around the agent’s tools, data, and actions.
What security layers should an agent have?
| Layer | What it protects | Useful controls |
|---|---|---|
| Identity and authorization | Systems and resources the agent can act on | Task-scoped roles, explicit allowlists, resource-level permissions, and separate read and write capabilities |
| Untrusted-input defenses | Decisions influenced by external content | Treat retrieved content as data, validate it, limit what actions remain possible, and check proposed actions |
| Data, memory, and secrets | Information exposed in context, stored between steps, or used for transactions | Minimized data access, isolated memory, retention bounds, audits, and mediated authentication |
| Network, tools, and environment | Connected systems and execution paths | Network segmentation, egress restrictions, assessed third-party tools, and separately restricted generated code |
| Action safeguards | High-impact, irreversible, or externally visible changes | Independent policy checks, human approval where appropriate, and separation of proposal from execution |
| Monitoring and evaluation | Misuse, unexpected behavior, and changing attack methods | Structured logs, anomaly monitoring, incident evidence, and repeatable adversarial tests |
These controls are complementary, not competing products or a formal standards scorecard. Their value depends on the agent’s task, authority, data exposure, connectivity, and the impact of its actions.
#1 Best Overall
How should permissions and tool access be limited?
Give each agent role only the tools and permissions needed for its task. Prefer explicit allowlists and resource-level scopes over broad standing access, and separate read from write capability where possible. Avoid default administrator or similarly broad roles. A prompt that says “do not delete files” is not equivalent to removing the agent’s delete permission.
Authorization should be enforced by a trusted application, policy engine, or tool boundary—not left to the model’s instructions. That boundary can evaluate the current user, task, target resource, and risk before allowing a tool call. Singapore government guidance recommends scoping execution privileges to need, avoiding default admin or sudo access, and blocking network access by default.
NIST NCCoE’s summary of stakeholder comments describes support for governance layers that evaluate agent requests against policy and transactional context, as well as identity metadata that captures operational boundaries and agent lineage. These are stakeholder feedback and open design discussions, not a final universal protocol specification.
Rank #2
How can an agent be protected from malicious instructions in a website or email?
Treat user-supplied and externally retrieved material—including websites, email, files, and tool or API responses—as untrusted input. NIST describes agent hijacking as indirect prompt injection: malicious instructions are placed in data the agent ingests, exploiting the lack of a clear separation between trusted instructions and external content.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →- Where the architecture allows, keep trusted control instructions distinct from retrieved content and label or handle external material as data.
- Validate inputs and constrain the agent’s available actions so that following malicious text does not grant it new authority.
- Check proposed outputs and actions at a boundary outside the model, and monitor tool use for unexpected behavior.
- Do not rely on a prompt filter to identify every attack; combine input handling with narrow permissions and action checks.
NIST also reports that attacks optimized for a model can expose weaknesses missed by previous evaluations. A clean result on one test should therefore not be treated as proof that the agent will resist a different attack.
How should sensitive data, memory, and credentials be handled?
Expose only task-required files and data, and carefully consider whether the agent needs personally identifiable or otherwise sensitive information at all. Isolate memory across users and sessions so one user’s context is not available to another. Validate information before persisting it, set retention and size bounds, and audit stored memory for sensitive material.
For workflows involving transactions, Singapore government guidance recommends virtual isolation and using a separate service for authentication and transactions rather than sharing credentials directly with the agent. This keeps the credential-bearing operation behind a service that can mediate it, instead of making a secret part of the agent’s direct control.
How should tools, networks, and execution environments be bounded?
Segment environments and network paths so a manipulated or compromised agent cannot freely reach unrelated systems. Allow access only to the connections needed for the task. Assess third-party tools before production use; Singapore government guidance recommends testing them in hardened sandboxes with syscall and network-egress restrictions. Restrict generated code separately and monitor its execution.
These controls complement the main sandbox. A sandbox alone does not prevent an agent from using an over-broad connected API that it is permitted to call, or from leaking data over a path the environment allows.
Rank #4
Which actions need approval or independent validation?
Put a separate check between the model’s proposed action and the mechanism that commits it. Use independent validation or human approval for actions with high impact—particularly irreversible, financial, administrative, or externally visible changes. OWASP recommends human oversight for high-risk actions and separating decision-making from execution for irreversible operations.
- Make approval specific to the proposed action and its target, rather than granting open-ended permission.
- Have a trusted policy check or approver evaluate the action before execution.
- Fail closed if policy evaluation or approval validation fails.
What should be logged, monitored, and tested?
For high-risk actions, record tool calls and their outcomes, relevant authorization decisions, approvals, and the policy versions in force. Monitor for anomalous behavior, unexpected sequences, and unusually high tool or compute consumption. Protect logs with access controls and redaction: logs can become another store of secrets if they capture sensitive inputs or credentials.
Preserve enough deployment and test context to investigate incidents and reproduce important decisions. OWASP recommends structured decision metadata for high-risk actions and evidence of tested versions, policies, abuse cases, and observed denials or approvals.
Test realistic indirect-injection, data-exfiltration, tool-abuse, and high-impact-action scenarios before release and after material changes to tools, permissions, prompts, retrieval, or models. Keep expected denials and abuse cases versioned, then rerun them as attack methods evolve. NIST CAISI emphasizes adaptive evaluations and task-specific attack performance rather than relying only on aggregate scores.
The importance of attack-specific testing is illustrated by one carefully scoped result: in CAISI’s 2025 evaluation of an upgraded Claude 3.5 Sonnet agent on AgentDojo tasks, the strongest baseline attack succeeded 11% of the time, while the strongest newly developed attack succeeded 81% of the time. Those figures apply to that model, task suite, and evaluation—not to AI agents generally.
How should teams choose and combine controls?
Use these dimensions to decide where a control belongs and how strong it needs to be. They synthesize OWASP and government guidance; they are a practical decision framework, not a formal standards scoring system.
- Enforcement point: Is a rule only prompt guidance, or is it enforced by a tool, policy, or infrastructure boundary?
- Authority scope: Does the agent have broad standing access, or only task- and resource-limited permission?
- Data exposure: Can it access unrestricted context and shared memory, or only minimized, isolated, time-bounded data?
- Action impact: Is the operation read-only and reversible, or does it write, communicate externally, move money, or make an irreversible change?
- Connectivity: Can the agent reach broad networks, or only segmented, allowlisted destinations?
- Assurance: Is there only a one-time test, or repeatable adversarial evaluation and audit evidence tied to versions and policy changes?
As impact rises, rely less on model intent and more on independent enforcement, review, and evidence. For example, an agent that only summarizes a document needs a different action boundary from one that can send messages or commit transactions; both still need appropriately limited access to their inputs and tools.
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What does NIST say about the stakes?
In a January 12, 2026 CAISI announcement, NIST stated: “AI agent systems are capable of planning and taking autonomous actions that impact real-world systems or environments.” That capability is why agent security has to address not just where code runs, but also what authority it receives, what can influence it, and how its actions are checked.
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