The best open-source approach is layered, not a single replacement. Use a runtime boundary such as NVIDIA OpenShell to isolate agents and govern network and credentials; add model-facing checks such as Meta LlamaFirewall; protect prompts, skills and MCP declarations at build time with Microsoft Agent Package Manager (APM); and use OWASP’s Agent Control Standard (ACS) as a portable control model. These projects address different enforcement points, so none should be treated as a complete substitute for every function of an enterprise platform.
Choose controls by tracing every way an agent can act: shell commands, file tools, MCP servers, APIs and outbound network requests. Permission dialogs alone are insufficient when an agent has been manipulated. Isolation, deny-by-default policy, restricted egress, human approval and independent audit storage remain necessary.
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What an “agent security platform” must actually control
AI-agent security is often discussed as if one scanner could solve it. In practice, controls sit at separate layers:
The Tool Desk
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- Model and tool middleware: inspects prompts, tool arguments, model output or generated code for jailbreaks, prompt injection and unsafe behavior.
- Runtime containment: limits operating-system access, files, processes, credentials and network destinations while the agent is running.
- Governance and evidence: records what happened, who authorized it and whether a human approved consequential actions.
An alternative that covers only one layer is valuable, but it is not a full replacement for controls at the others. For example, detecting a prompt injection does not stop a compromised process from reading files, and a process sandbox does not prove that an MCP package is trustworthy.
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OWASP’s Agent Control Standard (ACS), dated September 1, 2026, expresses the goal this way: “Agents must be inspectable, traceable and instrumentable—providing visibility into what they are, what they can access, what they did and why, and the ability to control their behavior at runtime.” Treat ACS as a standard and integration foundation, not as a ready-made product that automatically protects every framework.
Open-source options and the layer each one covers
| Project or guidance | Primary boundary | Documented capabilities | Important exclusions or cautions |
|---|---|---|---|
| NVIDIA OpenShell | Operating-system and runtime boundary | Isolated sandboxes, reduced privileges, limited filesystem reach, no direct network access, policy evaluation by an external supervisor, credential brokering, logging and policy-as-code. | Vendor-described capabilities, not an independent benchmark. It integrates with identity, secrets, observability and governance systems rather than replacing them. |
| Meta LlamaFirewall | Model and tool middleware | PromptGuard 2 for jailbreak detection, AlignmentCheck for possible goal hijacking or prompt-injection misalignment, and CodeShield for insecure or dangerous generated code. | AlignmentCheck is described as experimental in the April 29, 2025 paper. The framework does not, by itself, establish process isolation, filesystem limits or egress control. |
| OWASP Agent Control Standard | Portable middleware and runtime-control foundation | Hooks for inspectability, tracing, instrumentation and portable safety policies across agent frameworks. | A standard is not an out-of-the-box deployment. Individual frameworks must implement its hooks and controls. |
| Microsoft Agent Package Manager (APM) | Build-time context and dependency supply chain | Lockfiles, pinned commit SHAs, provenance and pre-deployment content-safety checks for prompts, skills, hooks and MCP declarations. | No runtime footprint. It does not sandbox MCP servers, perform malware analysis on dependency code or inspect behavior after context is ingested. |
| Microsoft Agent Framework safety guidance and FIDES reference | Application-level safety and information-flow design | Untrusted-content handling, indirect prompt-injection defenses, output validation, sensitive-session-data protection, resource limits and deterministic label-based controls before sensitive tools run. | Guidance for developers using that framework, not evidence of a general platform or cross-framework product. |
NVIDIA OpenShell: the closest open runtime boundary
NVIDIA describes OpenShell as an open-source runtime below agent frameworks and harnesses. Its stated design puts each agent in an isolated sandbox with reduced privileges, limited filesystem reach and no direct network access. A supervisor outside the sandbox evaluates network requests against policy and supplies credentials only where allowed. Allow and deny decisions are logged, and NVIDIA describes formal verification of policies against access boundaries.
This makes OpenShell most directly comparable with containment and runtime-policy layers. NVIDIA also describes policy-as-code, credential brokering, inference routing, audit and state handling, and controls for multi-agent workflows. Integrations with identity providers, secret stores, observability, security tooling and governance platforms are presented as complements; the runtime is not a replacement for those surrounding systems. NVIDIA’s FAQ states: “OpenShell is not another agent framework.”
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Where OpenShell fits
- Use it when the primary concern is what an agent can do to the host, files, network and credentials after it starts.
- Place policy outside the agent so a manipulated prompt cannot rewrite the enforcement point.
- Use the supervisor’s decision log as one input to a central audit system.
Questions to validate before adoption
- Which file paths, devices, processes and system calls are reachable from the sandbox?
- Are file tools and MCP servers inside the same boundary, or can they run outside it?
- How are identity-provider tokens and secrets issued, rotated and revoked?
- What happens when a policy denies a request: does the agent fail closed, retry, or receive sensitive policy details?
- Which agent frameworks and operating environments are currently supported?
Those answers require a proof of concept. Published capability descriptions are not equivalent to an independent security test.
Meta LlamaFirewall: model-facing guardrails
Meta’s April 29, 2025 paper presents LlamaFirewall as an open-source, system-level security framework for applications built with large language models. It describes three components:
PromptGuard 2
PromptGuard 2 is described as detecting jailbreak attempts. It operates on model-facing content, so it can help identify malicious instructions before or during a model interaction.
AlignmentCheck
AlignmentCheck audits reasoning for goal hijacking or misalignment induced by prompt injection. The paper explicitly labels it experimental. Treat its output as a signal requiring validation, not as proof that an agent is safe.
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CodeShield
CodeShield identifies insecure or dangerous generated code. This is useful when an agent writes or executes code, but code inspection is not a substitute for a sandbox, least-privilege account or network restriction.
LlamaFirewall should therefore sit in model and tool middleware. It can complement a runtime boundary and build-time provenance controls, but the described framework does not establish operating-system isolation, filesystem restrictions or outbound-traffic policy. Check the project repository and current documentation for implementation status and supported runtimes before deployment; the paper is evidence of the design and its caveat, not a current release matrix.
OWASP Agent Control Standard: a portable control foundation
ACS is useful when an organization runs more than one agent framework or wants a common control vocabulary. Its stated purpose is to expose middleware hooks through which platforms can provide inspection, tracing, instrumentation and runtime safety policies. That portability can reduce the risk of writing one-off controls tied to a single framework.
What ACS can provide
- A place to define what an agent is allowed to access and how those permissions are exposed for inspection.
- Hooks for recording actions and reasoning-related context in a consistent way.
- An integration target for policy enforcement across different agent frameworks.
What ACS cannot provide by itself
- A running sandbox or virtual machine.
- Automatic vetting of an MCP server, skill or prompt package.
- Proof that every framework implements every hook correctly.
Use ACS as an architecture and interoperability baseline, then verify each framework’s implementation and place hard isolation below the framework where possible.
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Microsoft Agent Package Manager addresses a different failure mode: an attacker changes a prompt, skill, hook or MCP declaration before an agent runs. APM treats those items as dependencies installed from version control into an agent project directory. Its documented controls include lockfiles, pinned commit SHAs, provenance and pre-deployment content-safety checks.
Recommended APM workflow
- Declare every prompt, instruction, skill, hook and MCP server as a versioned dependency rather than copying untracked files into the project.
- Resolve dependencies to immutable commit SHAs and generate the lockfile.
- Review provenance, license, maintainer and requested permissions before approving an update.
- Run content-safety checks before deployment and require review when a dependency changes.
- Store the lockfile with the application so a deployment can be reproduced and audited.
APM has no runtime footprint. Microsoft explicitly says it does not sandbox MCP servers at runtime, perform malware analysis on dependency code or inspect agent behavior after the agent has read its context. Pair it with runtime isolation, egress controls and centralized logging; APM is a context-integrity complement, not a runtime-platform replacement.
Microsoft Agent Framework guidance and FIDES-style controls
Microsoft’s Agent Framework safety guidance treats user, assistant and tool content as untrusted. It discusses indirect prompt injection, output validation, sensitive session data and resource limits, and points to FIDES for deterministic, label-based information-flow control before sensitive tools execute.
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A practical design is to attach labels to data and tool operations, then require a deterministic policy decision before a tool that can disclose, delete or modify sensitive information runs. This is an application control: it must be connected to the tool dispatcher and backed by an identity and audit system. It does not remove the need for a sandbox or network policy.
How to secure an agent without buying an enterprise platform
The following sequence combines the layers rather than assuming one project can cover them all.
1. Inventory every action path
- List shell and code-execution tools.
- List file-read and file-write tools, including their actual path reach.
- List MCP servers, startup commands, versions and reachable data.
- List API clients, browser or web-fetch tools and all outbound destinations.
- Identify credentials, tokens and identities available to each action.
If an action is absent from the inventory, it cannot be reliably governed.
2. Start with deny and allow only required actions
Keep policy in managed, reviewable configuration. Define allowed commands, directories, destinations, methods and data classes explicitly. Avoid granting broad permissions because an agent might need them later.
3. Put the agent in an isolation boundary
Use an OS sandbox, disposable virtual machine, development container or cloud environment. Verify the coverage rather than assuming the word “sandbox” means complete isolation: some environments constrain shell execution while file tools or MCP servers retain broader access. Test escape paths, mounted directories, inherited environment variables and device access.
4. Restrict outbound network traffic
Allow only destinations and protocols required for the task. Egress restriction limits data exfiltration after a successful prompt injection. Apply the rule to every process involved, including MCP servers and helper binaries, not just the primary agent process.
5. Vet and pin MCP dependencies
- Review the maintainer, source repository, license and release history.
- Read the server’s startup command and requested permissions.
- Identify the data the server can reach and whether it can send data elsewhere.
- Pin versions or commit SHAs and review changes to tool descriptions.
- Re-approve a server when its metadata, arguments or reachable data changes.
Tool descriptions, retrieved documents, web pages, issues, logs and tool responses are all untrusted input. A changed description can alter how an agent interprets a tool even when the executable itself is unchanged.
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6. Add model and output checks
Use guardrails such as PromptGuard 2, AlignmentCheck or CodeShield where their signals match your threat model. Validate tool arguments and generated code independently of the model’s explanation. Fail closed for malformed or out-of-policy requests.
7. Keep a human in the loop for irreversible actions
Require an explicit approval before deploying, deleting, sending messages, spending money, granting access or changing production configuration. Show the exact command, arguments, destination, data classification and identity that will be used. Do not treat a model-generated “approved” message as human authorization.
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Record tool calls, file writes, commands, network requests, policy decisions, identity and session context. Forward records to a central system that the agent cannot edit or delete, and avoid logging secrets. Test that denied actions are recorded as well as successful ones.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Comparison framework for a proof of concept
Published features are claims to validate. Build the same small test harness for each candidate and score the controls that matter to your deployment.
| Evaluation axis | Questions to answer |
|---|---|
| Enforcement boundary | Does the control act at build time, model/tool middleware, runtime, or more than one layer? |
| Isolation and filesystem | Can the agent or an MCP server reach the host, mounted secrets, devices or paths outside its task? |
| Network egress | Are destinations denied by default, and does the rule cover helper processes and MCP servers? |
| Identity and credentials | Are credentials brokered just in time, scoped to the task and withheld when policy denies access? |
| Policy portability | Can the same policy follow agents across frameworks, or is it tied to one harness? |
| Audit and trace | Are requests, arguments, decisions and outcomes captured with identity and session context outside agent control? |
| MCP and dependency handling | Are maintainers, versions, SHAs, startup commands, tool metadata and reachable data reviewable? |
| Deployment model | Can your team operate the required supervisor, sandbox, stores and logging infrastructure? |
| Documented maturity | Is the capability in a maintained release, an experiment, a standard draft or guidance only? |
| Explicit exclusions | What does the project expressly not sandbox, inspect, monitor or enforce? |
Minimum test cases
- Attempt a file read outside the declared workspace.
- Ask the agent to contact an unapproved domain and verify denial and logging.
- Change an MCP tool description and confirm that review is required.
- Inject a malicious instruction through a retrieved document or web page.
- Try to invoke a destructive tool without human approval.
- Check whether a denied request leaks credentials or sensitive policy details.
- Terminate and restart the agent, then verify that permissions and audit identity do not silently broaden.
No apples-to-apples independent benchmark is established for these alternatives. Your proof of concept should therefore measure the boundaries that matter to your environment instead of relying on feature checklists.
Common mistakes and how to correct them
Calling a guardrail a sandbox
A detector can flag suspicious text or code while the process still has unrestricted access. Add OS-level isolation and egress policy.
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Trusting permission prompts
OWASP’s DevSecOps guidance states: “Permission prompts are not a security boundary against a manipulated agent; isolation is.” Put enforcement outside the agent and constrain the process even when prompts are bypassed.
Sandboxing only shell execution
File tools, browser tools, MCP servers and helper processes may have separate paths to data. Test each path and place them under the same boundary or apply equivalent controls.
Installing unpinned tools
A server or skill can change after approval. Pin versions or SHAs, record provenance and re-review metadata and permissions on every update.
Keeping logs inside the agent environment
An agent that can delete its own evidence cannot support reliable incident response. Stream records to an external, access-controlled system.
Assuming framework guidance is universal
Application guidance may depend on one framework’s dispatcher, labels or hooks. Verify how the control behaves in your actual runtime and what happens when a tool bypasses the expected API.
Which combination fits which need?
| Primary need | Starting point | Add before production |
|---|---|---|
| Contain a coding or operations agent | Runtime sandbox and external policy supervisor | Egress restriction, credential brokering, human approvals and external audit. |
| Detect jailbreaks and unsafe generated code | LlamaFirewall components appropriate to the workflow | Independent argument validation, least privilege and runtime isolation. |
| Run agents across several frameworks | ACS-compatible middleware and a common policy model | Framework-specific implementation tests and a hard enforcement layer. |
| Control prompt, skill and MCP changes | APM lockfiles, SHAs, provenance and pre-deployment checks | Runtime sandboxing, dependency vetting and monitoring after context ingestion. |
| Protect sensitive information flows | Deterministic labels and FIDES-style checks before sensitive tools | Identity controls, output validation, egress policy and immutable audit. |
Bottom line
Open-source alternatives can replace portions of an enterprise AI-agent security stack, but the safe design is compositional. Use OpenShell or an equivalent runtime boundary for isolation and policy enforcement; LlamaFirewall for model-facing detection; APM for build-time context integrity; ACS for portable inspection and control hooks; and application-level information-flow and approval policies for sensitive actions. Validate every advertised capability against your own file, MCP, credential and network paths, because no single option described here provides a complete, independently benchmarked security platform.
Frequently Asked Questions
Can LlamaFirewall replace a sandbox?
No. Its documented components inspect jailbreaks, possible prompt-injection misalignment and generated code. They do not establish operating-system isolation, filesystem limits or network-egress control.
Does Microsoft APM protect an MCP server while it runs?
No. APM provides build-time dependency integrity through lockfiles, pinned SHAs, provenance and pre-deployment checks. Microsoft explicitly excludes runtime MCP sandboxing and post-ingestion behavior monitoring.
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What is the most important control after prompt injection?
Use isolation and restricted outbound traffic. Also require human approval for irreversible actions and send audit records to a system outside the agent’s control.
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