Veto vs Protect AI
Protect AI and Veto both sit in AI security, but they operate at different stages of the AI lifecycle. Protect AI (Recon, Guardian, Layer, ModelScan) secures models, the training pipeline, and the MLSec supply chain. Veto secures runtime: it intercepts the tool calls an agent tries to make and decides whether each one should execute. Model security vs action control.
Honest assessment
Protect AI is now part of Palo Alto Networks, with real depth in model scanning, red-teaming, and ML supply chain. Their ModelScan tool is the standard for catching malicious pickle payloads in model files. Veto does none of that. What Veto does is decide on the local policy path whether an agent should be allowed to call delete_account with these arguments, this user, this context. If your risk model is "compromised model file from Hugging Face," buy Protect AI. If it is "agent sent money to the wrong vendor," use Veto. The layers can coexist.
What each product does
Protect AI
An ML security product line whose suite covers model scanning (Guardian, ModelScan), red-team and adversarial testing (Recon), and runtime LLM guardrails (Layer). Strong roots in the MLSec community and a long bug-bounty history on open-source ML tooling.
Core capability:
Scan model files for malicious serialization, vet third-party models before they enter your registry, red-team your model for jailbreaks and data leakage, and filter prompts and responses at the LLM boundary.
- Model file scanning for serialization exploits
- Adversarial red-teaming of model behavior
- Prompt injection and content filters at the LLM edge
- ML supply chain monitoring
- Open-source ModelScan scanner
Veto
An open-source runtime authorization SDK for AI agents. Sits inside the agent process, intercepts each governed tool call before execution, and evaluates it against YAML policies that live in your repo.
Core capability:
Decide allow, deny, or require human approval for each tool invocation based on tool name, arguments, user context, and time. Approvals integrate with configured review channels, email, or your own approval UI. Each governed decision is recorded for review.
- Tool-call interception and policy evaluation
- Human review workflows
- Declarative YAML policies in version control
- SDK and framework integrations including MCP
- Apache-2.0 SDK, self-hostable policy engine
Feature comparison
Most of these rows are not a feature checklist. They are entirely different categories of work. Included so you can see exactly where the products do and do not overlap.
| Capability | Veto | Protect AI |
|---|---|---|
| Tool-call authorization | ||
| Human approval workflows | ||
| YAML policy engine | ||
| Model file scanning | ||
| Adversarial red-teaming | ||
| ML supply chain inventory | ||
| Prompt injection filters | ||
| Decision record per action | ||
| Open-source core | ||
| Framework integrations (LangChain, OpenAI, MCP) | ||
| Self-hosted policy engine | ||
| In-process decision path |
Choose by lifecycle stage
The SDLC for AI has three rough phases. Each one has different risks and different tools.
Phase 1: Building and releasing the model
Risk: a model file with embedded malicious code, weights poisoned during training, a finetune that has memorized PII. This is where Protect AI lives. Guardian and ModelScan check serialization. Recon stress-tests the model with jailbreaks. The answer to "is this model ready to deploy" is yes or no before deployment, not at each request.
Phase 2: Serving the model to requests
Risk: prompt injection, jailbreaks, sensitive data leaking back to users. This is a content moderation problem. Protect AI Layer plays here, alongside Lakera, AWS Bedrock Guardrails, and similar. Veto does not.
Phase 3: The agent executes tool calls
Risk: the model decides to call refund_order with the wrong order ID, send an email to the wrong list, run a shell command, push to main. This is where Veto sits. Tool-call interception, policy decision, audit log. None of the MLSec tools cover this layer because it is not a model problem, it is an action problem.
Pricing
Open Source runs locally and the policy engine remains Apache-2.0. Core adds shared approvals, decision records, export, and retention. See the pricing page for price, retention, and action limits.
Commercial pricing runs through Palo Alto Networks procurement; verify the current package directly. ModelScan is an open-source scanner. Guardian, Recon, and Layer are commercial offerings handled through vendor procurement.
Decision framework
Choose Veto when
- You build agents that execute tool calls with real consequences
- You need allow, deny, or approve decisions at the function-call boundary
- Review teams need decision records showing which actions ran and which were blocked
- You want policies as YAML in your repository, reviewable like code
- You build in TypeScript or Python and want a native SDK
Choose Protect AI when
- You pull models from Hugging Face or other external registries
- You need to red-team a fine-tuned model before deploying it
- Your security team wants ML supply chain inventory and CVE tracking
- You need prompt-injection and content filters at the LLM edge
- You are already a Palo Alto customer and want platform consolidation
Frequently asked questions
Does Protect AI block agent tool calls at runtime?
Should I use Protect AI Guardian or Veto?
What about Protect AI Layer and Veto overlap?
Is Veto open source like ModelScan?
Related comparisons
Prompt filtering vs action control
Veto vs Guardrails AIOutput validation vs authorization
Veto vs Bedrock GuardrailsContent filters vs tool authorization
Going deeper: Runtime agent authorization, The authorization gap, Blog: the authorization gap.
Securing the model is half the job. Veto covers the other half.