ToolGuard Models

Industry-Leading AI Security for MCP Ecosystems

Runlayer ToolGuard is an industry-leading suite of specialized machine learning models that protect your MCP environment from tool poisoning, prompt injection, and output manipulation attacks. With fast 50-100ms inference times, ToolGuard delivers real-time threat detection without compromising performance.

Currently featuring three specialized threat classification models, with additional models in active development to address emerging attack vectors.

The Models

Tool List Guard

Scans tool definitions at registration to detect risky descriptions, prompt injection attempts, and hidden instructions before tools are made available to your environment.

Tool Call Guard

Scans tool execution outputs in real-time to detect risky responses, data exfiltration attempts, and prompt injection before they reach your LLMs.

Tool Intent Guard

Detects tool intent drift from prompt injections that lead to data exfiltration, privilege escalation, credential theft, and infrastructure damage. Unlike the Tool Call Guard which evaluates individual responses, Tool Intent Guard analyzes tool inputs and outputs together to detect semantic misalignment — catching cases where a tool’s actual behavior diverges from what was requested.

Skill File Scanning

ToolGuard can also scan skill files uploaded to the platform. Each file’s content is analyzed using the same threat classification models, with results cached per content hash and scanner version. Large files are automatically chunked for processing. Skill file scanning runs when skills are uploaded via the CLI (skills push), imported from a GitHub URL, or created/updated via the API, producing per-file risk scores and an overall skill-level classification.

Skill Risk Policy

Admins can configure how the platform responds when a skill scan detects elevated risk. Navigate to Settings → Security Scanners to set the action for each risk tier:

Risk tier	Default action	Options
High	Block	Block, Alert, Allow
Medium	Alert	Block, Alert, Allow

Block — the skill import is rejected
Alert — the skill is imported with a warning badge visible in the UI; acceptance is logged to the Audit Log
Allow — the skill is imported without restriction

Low and Minimal risk skills are always allowed. These settings apply globally to all skill imports (CLI, API, web UI, and web import from GitHub).

LLM Risk Categorization

When Tool List Guard flags a tool at Medium or High risk, an LLM-based categorizer automatically classifies the threat into specific attack categories. The full taxonomy includes:

Prompt Injection
Data Exfiltration
Privilege Escalation
Destructive Action
Unauthorized Communication
Resource Abuse
Shadow Persistence
Context Poisoning
Guardrail Bypass
Supply Chain Compromise

Categories are not mutually exclusive — a single tool can match multiple categories. These labels appear in security violation details and audit logs, replacing generic “risky tool” messages with actionable context so you know what kind of threat was detected.

LLM categorization requires the Bedrock integration to be enabled in your deployment. When disabled, violations retain the default ToolGuard reason.

What a Finding Shows

Findings explain the reasoning behind a risk assessment, not just a score. Each Tool List Guard finding includes:

Confidence score and risk tier — the model’s 0.0–1.0 score, mapped to a tier (see Risk Tiers)
Threat categories — the attack categories assigned by LLM Risk Categorization (above)
Areas of concern — when a tool scores above the risk threshold, ToolGuard runs an additional analysis pass that narrows down the specific excerpt of the tool definition that triggered the detection. This excerpt is stored with the finding and displayed under Area of concern on the connector detail page, alongside the tool’s name and full description — so reviewers see exactly which text caused the flag.

Tool Call Guard works the same way for blocked outputs: the finding includes the specific portion of the tool output that triggered the block, not just the score.

Additional specialized models are in development to address new attack vectors as they emerge in the MCP ecosystem.

Why Industry-Leading?

Purpose-Built for MCP - Custom-trained threat classification models specifically designed for MCP ecosystem attacks. High Performance - Fast inference with typical scan times of 50-100ms. Continuously Evolving - Models are regularly refined based on emerging threat patterns. Battle-Tested - Deployed in production environments protecting real-world MCP deployments. Enterprise-Ready - Complete audit logging, flexible configuration, and Security Dashboard integration.

Configuration

Navigate to Settings → Security Scanners to enable Runlayer ToolGuard models.

Scanner coverage

Different scanners run at different points in the agent flow:

Surface	Scanner coverage
Tool definitions	Tool List Guard scans names, descriptions, and schemas when a connector is registered or re-scanned.
Tool inputs	PII, credential, invisible-character, and Tool Intent Guard checks can inspect tool arguments before execution.
Tool outputs	Tool Call Guard, PII, credential, invisible-character, and Tool Intent Guard checks can inspect results before they reach the model.
Skill files	Skill file scanning runs on uploaded skill content before import.
Session trajectory	AgentGuard evaluates multi-turn behavior across prompts, reasoning, tool output, and follow-up reasoning.

User prompts and model responses appear in Sessions when full session scanning is enabled. Per-call ToolGuard scanners focus on MCP and local tool traffic; AgentGuard is the session-level scanner for behavior drift across the conversation.

Sensitivity Levels

Each scanner phase (Tool List Guard, Tool Call Guard, Tool Intent Guard) has a configurable sensitivity that controls how aggressively it flags findings:

Level	Behavior
Strict	Lowest tolerance — flags more items, fewer false negatives
Balanced (default)	Recommended for most environments
Moderate	Highest tolerance — fewer flags, useful for noisy connectors

Sensitivity is set globally in Settings → Security Scanners and can be overridden per connector in the connector’s security settings, or per client (browser, CLI, IDE) in the Per-client overrides section. Connectors without an explicit override inherit the global value.

Scanner tuning is global, per-connector, or per-client. Public docs do not document per-user, per-group, or per-agent scanner thresholds. For self-hosted deployments, Tool List Guard risk-tier thresholds can also be tuned with the environment variables listed under Risk Tiers.

Violation Actions

Each scanner has a configurable action that controls what happens when it detects a finding. The available actions depend on the scanner type:

Action	Behavior
Block	Reject the request — the tool call does not proceed
Block (Self-Approve)	Block the request but allow the caller to approve and retry (per-connector only, PII scanner)
Mask	Redact or strip the detected content, then let the request continue
Alert	Log the detection but take no blocking action — the request proceeds unchanged
Allow	Disable the scanner entirely

Not every scanner supports every action. The table below shows which actions are available for each scanner:

Scanner	Allow	Alert	Mask	Block
PII detection	Yes	Yes	Yes (redact PII values)	Yes
Invisible character detection	Yes	Yes	Yes (strip hidden chars)	Yes
Credential detection	Yes	Yes	Yes (mask credentials)	Yes
ToolGuard ML scanners (Tool List Guard, Tool Call Guard, Tool Intent Guard)	Yes	Yes	—	Yes
Skill risk policy	Yes	Yes (warn on import)	—	Yes

Defaults: PII detection defaults to Alert. Invisible character detection and credential detection default to Mask. ToolGuard ML scanners default to Block. Actions are set globally in Settings → Security Scanners and can be overridden per connector or per client.

Custom Security Messaging

When a scanner blocks a call, Runlayer returns an explanation to the calling agent (and the user). By default this ends with generic guidance to contact your Runlayer administrator. Admins can replace that guidance with their own — for example, pointing users to a specific Slack channel or internal runbook instead of an inbox. Navigate to Settings → Security Scanners → Edit Security Messaging. Each message has a fixed prefix that is always shown (the violation details and audit-log link); your custom text replaces only the closing What to do guidance. A live preview shows exactly what agents will receive. The following messages can be customized:

Message	Shown when
Security block	A security scanner blocks a tool call
Self-approve	A block can be approved and retried by the caller
Policy denial	A policy denies a tool call or resource access
Connector disabled	Tools on a disabled connector are called
No access	Someone connects to a connector they have no access to

Messages support a small set of {{variable}} placeholders — such as {{server_name}}, {{tool_name}}, {{reason}}, and {{audit_log_link}} — that are filled in at runtime. The editor lists the variables available for each message. Leaving a message unset falls back to the built-in default.

PII Detection

PII scanning uses pattern-based detection with validators to identify sensitive data in MCP traffic. The following built-in PII types are detected:

Type	Description
SSN	US Social Security numbers
Credit Card	Card numbers (validated with Luhn check)
Phone	Phone numbers (with context validation)
Email	Email addresses
Passport	Passport numbers (with context validation)
Driver’s License	US driver’s license numbers
IBAN	International Bank Account Numbers
IP Address	IPv4 addresses
Date of Birth	Dates of birth (with context validation)
MRN	Medical Record Numbers
VIN	Vehicle Identification Numbers (with checksum validation)
Bitcoin	Bitcoin wallet addresses
Ethereum	Ethereum wallet addresses

Admins can also add custom PII rules with regex patterns in Settings → Security Scanners. Custom rules run alongside built-in patterns and appear in audit logs with a CUSTOM label. Individual built-in types can be disabled per organization. PII masking redacts matched values before the request continues. Public docs do not currently describe stable placeholder substitution that preserves the same replacement token for the same PII value across a session.

PII Scan Direction

PII detection can be applied to tool inputs, outputs, or both. The direction controls which traffic the PII scanner inspects:

Direction	Behavior
Input (default)	Scans data sent to MCP tools
Output	Scans data returned from MCP tools
Both	Scans in both directions

Set the direction globally in Settings → Security Scanners and override per connector in the connector’s security settings, or per client in the Per-client overrides section. Connectors without an override inherit the global value.

Risk Tiers

Tool List Guard assigns a risk tier to each scanned tool based on its confidence score. The default thresholds are:

Tier	Score Range	Meaning
Minimal	< 0.6	Clean scan, no concern
Low	0.6 – 0.7	Low-confidence flag
Medium	0.7 – 0.9	Elevated risk, review recommended
High	≥ 0.9	High-confidence detection

Self-hosted deployments can tune these thresholds via environment variables:

RUNLAYER_TOOL_GUARD_LIST_RISK_THRESHOLD_LOW (default 0.6)
RUNLAYER_TOOL_GUARD_LIST_RISK_THRESHOLD_MEDIUM (default 0.7)
RUNLAYER_TOOL_GUARD_LIST_RISK_THRESHOLD_HIGH (default 0.9)

If you don’t see Runlayer ToolGuard options, you need to configure your Runlayer deployment to enable the GPU ToolGuard infrastructure. See deployment documentation for setup instructions.

Submitting Feedback

When a ToolGuard scan result is wrong — a benign tool flagged as risky (false positive) or a risky one that slipped through (false negative) — you can report it back to Runlayer. Feedback is used to improve future model accuracy. Submit feedback to the same API surface as the scoring endpoints, authenticated with an organization API key that has the Security Scan role (sent in the x-runlayer-api-key header):

curl -X POST "$RUNLAYER_BASE_URL/api/v1/security/feedback" \
  -H "x-runlayer-api-key: $RUNLAYER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "endpoint": "tool_call",
    "feedback_type": "false_positive",
    "human_label": "allow",
    "notes": "Internal reporting tool — the output is expected, not exfiltration.",
    "scan_context": {
      "tool_name": "generate_report",
      "score": 0.94,
      "decision": "block"
    }
  }'

Field	Required	Description
`endpoint`	Yes	Which score endpoint the feedback is about: `tool_list`, `tool_call`, `tool_guard_io`, or `skill`
`feedback_type`	Yes	`false_positive` (flagged but safe) or `false_negative` (missed a real risk)
`human_label`	Yes	The label you believe is correct. Canonical values are `block` / `allow`, but any short label is accepted
`notes`	No	Free-form context explaining the correction
`scan_context`	No	The scanned payload plus the score/decision you disagree with, echoed back for triage

A successful submission returns 201 with a submission_id. Feedback is retained per organization for model review.

Feedback submission isn’t available in certain self-hosted cloud environments. Where it’s unavailable, the endpoint returns 503.

Monitoring

Security Dashboard - View detection timelines, violation trends, and common threat types Connector Pages - Tool List Guard warnings appear directly on connector detail pages when potentially risky tools are detected Audit Logs - Full history of detections, blocks, and configuration changes with confidence scores Sessions - Review scanner outcomes in full AI session timelines AgentGuard - Session-level behavior monitoring across the agent trajectory

Best Practices

Use per-server overrides for high-risk external servers
Use per-client overrides when specific AI clients (e.g., IDE vs. browser) need different scanner behavior
Combine with MCP access policies for layered security
Review flagged tools with your security team before blocking

Staying Ahead

Runlayer ToolGuard models are continuously refined based on emerging threat patterns in the MCP ecosystem. Our commitment to continuous innovation ensures you have industry-leading defenses as new attack techniques emerge. Deployed model versions are tracked in the ToolGuard Model Versions changelog.

Model Attribution

The Runlayer ToolGuard suite utilizes GA Guard Lite for threat classification embeddings and model inputs. GA Guard Lite is licensed under Apache 2.0.

Security Best Practices

MCP security guidelines and recommendations

Audit Logs

View detailed activity and security logs

Sessions

Monitor scanner outcomes in AI session timelines

AgentGuard

Session-level behavior monitoring across the agent trajectory

Access Policies

Configure MCP access control policies

Enable

Control

Self-Hosted Deployment

Operations

Cookbook

Integrations

Connectors

Resources

Industry-Leading AI Security for MCP Ecosystems

The Models

Tool List Guard

Tool Call Guard

Tool Intent Guard

Skill File Scanning

Skill Risk Policy

LLM Risk Categorization

What a Finding Shows

Why Industry-Leading?

Configuration

Scanner coverage

Sensitivity Levels

Violation Actions

Custom Security Messaging

PII Detection

PII Scan Direction

Risk Tiers

Submitting Feedback

Monitoring

Best Practices

Staying Ahead

Model Attribution

Security Best Practices

Audit Logs

Sessions

AgentGuard

Access Policies

​Industry-Leading AI Security for MCP Ecosystems

​The Models

​Tool List Guard

​Tool Call Guard

​Tool Intent Guard

​Skill File Scanning

​Skill Risk Policy

​LLM Risk Categorization

​What a Finding Shows

​Why Industry-Leading?

​Configuration

​Scanner coverage

​Sensitivity Levels

​Violation Actions

​Custom Security Messaging

​PII Detection

​PII Scan Direction

​Risk Tiers

​Submitting Feedback

​Monitoring

​Best Practices

​Staying Ahead

​Model Attribution

​Related Resources

Security Best Practices

Audit Logs

Sessions

AgentGuard

Access Policies

Industry-Leading AI Security for MCP Ecosystems

The Models

Tool List Guard

Tool Call Guard

Tool Intent Guard

Skill File Scanning

Skill Risk Policy

LLM Risk Categorization

What a Finding Shows

Why Industry-Leading?

Configuration

Scanner coverage

Sensitivity Levels

Violation Actions

Custom Security Messaging

PII Detection

PII Scan Direction

Risk Tiers

Submitting Feedback

Monitoring

Best Practices

Staying Ahead

Model Attribution

Related Resources