Security audit

Expanso secrets-scan

Security checks for vulnerabilities and agentic risk

Overview

This secret-scanning skill has a legitimate purpose, but it sends sensitive scan input to OpenAI and exposes an unauthenticated network service without clear enough disclosure or controls.

Review this carefully before installing. Do not use it on production secrets, private code, logs, or regulated data unless your organization explicitly permits sending that content to OpenAI. If using MCP mode, bind it to localhost or put it behind authentication and rate limits. Treat the advertised local/no-key backend as unimplemented in the inspected pipelines.

A.I.G

Vulnerability Patterns

Insecure Skill Coding PracticesFinds exploitable flaws such as hardcoded secrets or command injection
Skill Instruction HijackingAlters the agent's session goals or safety constraints when the skill loads
Agent Memory PoisoningWrites attacker-controlled rules into memory that affect later sessions
Remote Payload Retrieval and ExecutionFetches external code whose behavior can change after review
Embedded Malicious CodeShips malicious scripts inside the skill and executes them locally

Findings (4)

other

Error

Location: pipeline-cli.yaml:29
Finding: Raw secrets and source content are transmitted to a remote LLM
Content: View full analysis
)\n- Environment variable references (${VAR})\n- Example values from documentation\n\nReturn valid JSON only.\n\nText to scan:\n```\n" + content() + "\n```" } ] - openai_chat_completion: api_key: "${OPENAI_API_KEY}" model: gpt-4o-mini ``` `pipeline-mcp.yaml:29-48`: ```yaml let secret_types = this.types.or(["api_key", "token", "password", "private_key"]).join(",") root.messages = [ { "role": "system", "content": "You are a security scanner specialized in detecting hardcoded secrets. Be thorough but avoid false positives. Do NOT flag placeholder values." }, { "role": "user", "content": "Scan for: " + $secret_types + "\n ...[truncated 2426 chars]
Remediation: View remediation

T09 · Insecure Skill Coding Practices

Error

Location: skill.yaml:13
Finding: Local-processing and optional-credential claims contradict the implemented behavior
Content: View full analysis
Remediation: View remediation

T09 · Insecure Skill Coding Practices

Error

Location: pipeline-mcp.yaml:8
Finding: Unauthenticated MCP endpoint is exposed on all network interfaces
Content: View full analysis
Remediation: View remediation

T09 · Insecure Skill Coding Practices

Error

Location: pipeline-cli.yaml:36
Finding: Prompt injection can suppress secret findings or force fail-open parsing
Content: View full analysis
)\n- Environment variable references (${VAR})\n- Example values from documentation\n\nReturn valid JSON only.\n\nText to scan:\n```\n" + content() + "\n```" } ] - openai_chat_completion: api_key: "${OPENAI_API_KEY}" model: gpt-4o-mini ``` `pipeline-cli.yaml:53-54`: ```yaml let raw = this.choices.0.message.content let parsed = $raw.parse_json().catch({"findings": [], "summary": "Parse error"}) ``` Equivalent vulnerable MCP construction and parsing are present at `pipeline-mcp.yaml:38-42` and `pipeline-mcp.yaml:50-51`: ```yaml "content": "Scan for: " + $secret_types + "\n\nReturn JSON:\n{\n \"findings\": [{\"type\": \"...\", \"value\": \"partial\", \"line\": 1, \"severity\": \"high|medium|low\"}],\n \"summary\": \"...\"\n}\n\nText:\n```\n" + $text + "\n```" ``` ```yaml let raw = this.choices.0.message.content let parsed = $raw.parse_json().catch({"findings": []}) ``` ### Technical Analysis Untrusted files and request text are concatenated directly into a natural ...[truncated 1730 chars]
Remediation: View remediation

SkillSpector

Vulnerability Patterns

Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
Trigger AbuseOverly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code

Findings (7)

Natural-Language Policy Violations

High

Category: Not specified by scanner
Confidence: 98% confidence
Finding: The prompt explicitly asks the model to return the full matched secret string, which causes any detected credential in stdin to be reproduced in the model response and then emitted to stdout as JSON. This increases exposure of sensitive values, creates a secondary exfiltration path to the external LLM provider, and can leak secrets into logs, terminals, downstream tooling, or saved command output.
Content: No source excerpt is available for this finding.

Missing User Warnings

Medium

Category: Not specified by scanner
Confidence: 94% confidence
Finding: The README encourages scanning arbitrary code, configs, and logs using a model-backed pipeline but does not clearly warn users that the scanned content may be transmitted to an external model service. Because this skill is specifically designed to process highly sensitive material such as secrets, users may unknowingly send credentials or proprietary code off-host, creating confidentiality and compliance risk.
Content: No source excerpt is available for this finding.

External Transmission

Medium

Category: Data Exfiltration
Confidence: 60% confidence
Finding: Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
Content: Scanner excerpt · README.md (reported line 33)May include surrounding context.
bash
PORT=8080 expanso-edge run pipeline-mcp.yaml & curl -X POST http://localhost:8080/scan \ -H "Content-Type: application/json" \ -d '{ "text": "const API_KEY = \"sk-abc123def456\";",

Vague Triggers

Medium

Category: Not specified by scanner
Confidence: 94% confidence
Finding: The pipeline exposes an HTTP service on 0.0.0.0 with a POST /scan endpoint and no visible authentication, authorization, or network restriction. Because the endpoint forwards arbitrary submitted text to an external LLM, an attacker can invoke the service remotely for unauthorized use, induce cost, and potentially exfiltrate sensitive submitted content to the model provider.
Content: No source excerpt is available for this finding.

Vague Triggers

Low

Category: Not specified by scanner
Confidence: 82% confidence
Finding: The manifest's commented usage shows the pipeline being run on any content piped to stdin, but it does not define narrower trigger conditions, exclusions, or negative examples. For a manifest file, this can make the activation scope ambiguous and increase the chance of unintended use on sensitive inputs.
Content: No source excerpt is available for this finding.

Natural-Language Policy Violations

Low

Category: Not specified by scanner
Confidence: 78% confidence
Finding: The prompt literal hard-codes English response fields and instructions such as "Return JSON" and fixed English keys/values, without offering any user language or locale choice. Under the policy, forcing a specific language without opt-in can be a natural-language policy violation unless clearly justified.
Content: No source excerpt is available for this finding.

Vague Triggers

Low

Category: Not specified by scanner
Confidence: 83% confidence
Finding: This YAML manifest contains only a generic name and description for the skill tests, with no specific activation phrases, scope limits, or negative examples. For manifest files, that can make invocation conditions ambiguous because nothing in the file narrows when the skill should or should not activate.
Content: No source excerpt is available for this finding.

Static analysis

Detected: suspicious.exposed_secret_literal

File appears to expose a hardcoded API secret or token.

Critical

Code: suspicious.exposed_secret_literal
Location: README.md:67

File appears to expose a hardcoded API secret or token.

Critical

Code: suspicious.exposed_secret_literal
Location: test/test.yaml:9