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Security audit

n8n

Security checks for vulnerabilities and agentic risk

Overview

This skill is not clearly malicious, but it substantially overstates implemented capabilities and has unsafe installation and verification patterns users should review before installing.

Treat this as a review-needed package. Do not rely on its claimed security gates, self-improvement, model fallback, or persistent-agent behavior unless the publisher supplies real implementation code and tests. If installing anyway, review the installer first, avoid latest-based commands, use an isolated environment, pin dependencies, and verify the package identifier and publisher.

Vulnerability Patterns
  • Insecure DependenciesIntroduces malicious components through unsafe dependency sources
  • 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
Findings (4)

T08 · Insecure Dependencies

Error
Location
install.sh:21
Finding

Unpinned third-party packages are executed during installation

Content
View full analysis
/dev/null || true ``` The documentation also instructs users to execute a mutable npm package version: ```bash npx clawhub@latest install agentic-ai-gold ``` ### Technical Analysis The installer downloads and executes six Python packages without exact version constraints, a lockfile, or package hashes. The documented `npx clawhub@latest` command similarly executes whichever release is tagged as `latest` at execution time. Python and npm package installations can execute package-controlled installation or build logic. Consequently, the effective code executed during installation can change after this project has been audited. The command also redirects dependency errors to `/dev/null` and uses `|| true`. This suppresses both installation errors and dependency-resolution failures, leaving the environment in an unknown or partially installed state while installation continues. No evidence establishes that the currently named dependencies are malicious. The vulnerability is that their identities and contents are not reproducibly constrained or verified. ### Attack Path 1. An attacker compromises a referenced package, its maintainer account, or the associated package registry. 2. Alternatively, an unsafe or incompatible new release is published under one of the referenced names. 3. A user follows the project documentation or runs `install.sh`. 4. `pip` or `npx` downloads the mutable package release. 5. Package-controlled installation logic executes with the privileges of the user running the installer. 6. Errors can be hidden by `2> ...[truncated 744 chars]
Remediation
View remediation
/dev/null || true`; terminate installation when dependency installation fails. 7. Verify package origin, ownership, and signatures where the package ecosystem supports them. 8. Run dependency vulnerability and provenance checks in CI before publishing. ]]>

T08 · Insecure Dependencies

Warning
Location
README.md:34
Finding

README directs users to a different package identifier

Content
View full analysis
Remediation
View remediation

T09 · Insecure Skill Coding Practices

Error
Location
install.sh:29
Finding

Environment-controlled skill path permits Python command injection

Content
View full analysis
Remediation
View remediation

other

Error
Location
install.sh:33
Finding

Installer and examples fabricate successful security verification

Content
View full analysis
0.1 # 90% pass rate status = "✅ PASS" if success else "❌ FAIL" if success: passed += 1 print(f" {status} {test}") print(f"\n Results: {passed}/{len(tests)} tests passing") ``` ### Technical Analysis The installer places an unconditional sequence of `print` statements inside a `try` block. It does not import `agentic_ai`, instantiate a council, validate dependencies, exercise fallback providers, test security gates ...[truncated 2566 chars]
Remediation
View remediation
Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • Rogue AgentSelf-Modification, Session Persistence
  • MCP Tool PoisoningHidden Instructions, Unicode Deception, Parameter Description Injection
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (19)

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared description presents a substantive AI framework with self-improvement, security gating, resilience features, and research-backed infrastructure. The supplied code is only an example/demo script with a mock Council class that stores member metadata and prints simulated status and canned responses. References to '17 dharmic gates', '4-tier model fallback', '5-layer memory', and 'self-improvement' are only displayed text, not implemented behavior. Therefore the actual code materially underdelivers on the claimed primary purpose and capabilities.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared description presents a sophisticated self-improving AI framework with substantial security, resilience, and research capabilities. The supplied code chunk is only an example script demonstrating simulated specialist-agent creation and execution. Its 'dharmic gates' are printed messages over a short hardcoded list, task execution is mocked via canned strings, and there is no evidence of self-improvement, real infrastructure behavior, resilience layers, or access to any research corpus. This is a material description-to-behavior mismatch, not merely omitted implementation detail.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

There is a material mismatch between the description and the code chunk. The description presents a real self-improving AI infrastructure with substantive capabilities, but the supplied code is explicitly an example/demo that simulates those behaviors. Its primary behavior is printing a narrated mock workflow and storing simple in-memory counters/history. It does not perform real self-modification, external research retrieval, validation, infrastructure control, or command execution. Therefore the declared description overstates what this code actually does.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
99% confidence
Finding

The declared description presents a substantive self-improving AI framework with security, resilience, and research capabilities. However, the supplied code chunk is only a demonstration script that prints static messages indicating those systems are active. It does not implement self-improvement, security enforcement, fallback logic, memory systems, or agent operations. Because the actual code behavior is primarily cosmetic/demo output rather than the described functionality, this is a material description-to-behavior mismatch.

Content

No source excerpt is available for this finding.

Tp4

High
Category
MCP Tool Poisoning
Confidence
95% confidence
Finding

The declared description presents the skill as an autonomous self-improving AI framework with specific security and resilience properties. The supplied code chunk does not implement those capabilities; it primarily performs environment setup and package installation. While an installer can support such a framework, this chunk's actual purpose is materially different from the declared primary purpose. It also writes to the filesystem and invokes pip, which are concrete behaviors not reflected in the description or permissions. Therefore the description does not accurately represent what this code chunk actually does.

Content

No source excerpt is available for this finding.

Rp1

Medium
Category
MCP Rug Pull
Confidence
94% confidence
Finding

The quickstart instructs users to execute npx clawhub@latest install agentic-ai-gold, which pulls and runs the latest package code at install time without a pinned, reviewed version. This creates a supply-chain risk: if the package or one of its release channels is compromised, users following the quickstart may immediately execute attacker-controlled code on their systems.

Content

No source excerpt is available for this finding.

Rp1

Medium
Category
MCP Rug Pull
Confidence
93% confidence
Finding

The README instructs users to run npx clawhub@latest install agentic-ai, which pulls and executes the latest remote package version without pinning. This creates a supply-chain risk: if the package is compromised or a malicious update is published, users may execute attacker-controlled code during installation.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
88% confidence
Finding

The README explicitly promotes self-rewriting and overnight update behavior but does not warn users that the system may modify code or system state autonomously. In an agent framework, self-updating behavior materially increases the risk of unauthorized changes, persistence of bad logic, and difficult-to-audit compromise paths if the update pipeline is poisoned or misaligned.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
91% confidence
Finding

The README advertises native access to 10,000+ MCP servers and broad agent-to-agent connectivity while emphasizing seamless integration, but it omits clear warnings about data exposure, tool trust, and host-system integrity. In this context, encouraging broad external tool access without caution can lead users to over-trust remote servers that may exfiltrate secrets, manipulate agent behavior, or trigger unsafe actions.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The example states that agents terminate after task completion, but every Specialist instance is appended to the class-level _registry and never removed. In a long-running process or if this pattern is copied into production code, completed agents and their task metadata will accumulate indefinitely, causing memory growth and potentially retaining sensitive task information longer than intended.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The docstrings describe overnight research, evaluation, and self-updating behavior, but the code only simulates these phases locally and records fabricated results. This mismatch is dangerous because developers may rely on nonexistent safeguards such as integration testing, dharmic validation, or approval gates, creating false assurance around an agentic system that is marketed as self-improving.

Content

No source excerpt is available for this finding.

Intent-Code Divergence

Medium
Category
Not specified by scanner
Confidence
98% confidence
Finding

The example explicitly claims 'This is not metaphorical. It's operational.' even though the implementation only prints simulated activity driven by random values and does not perform real research, testing, or self-modification. In a security-sensitive agent framework, overstating autonomous capability can mislead users into enabling or trusting workflows they believe are validated or improving themselves, weakening informed consent and safe deployment decisions.

Content

No source excerpt is available for this finding.

Session Persistence

Medium
Category
Rogue Agent
Confidence
60% confidence
Finding

Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.

Content

Scanner excerpt · install.sh (reported line 25)May include surrounding context.

sh
echo "📦 Installing dependencies..."
pip install -q langgraph openai-agents crewai pydantic-ai mem0 zep-python 2>/dev/null || true

# Create config directory
mkdir -p ~/.agentic_ai/config

# Copy skill files

Rp1

Medium
Category
MCP Rug Pull
Confidence
96% confidence
Finding

The skill instructs users to execute npx clawhub@latest install agentic-ai-gold, which fetches and runs the latest remote package version without pinning or integrity verification. This creates a supply-chain execution risk: if the package or publishing account is compromised, users may run attacker-controlled code immediately during installation.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
89% confidence
Finding

The skill explicitly markets autonomous nightly scanning, testing, update proposal, and self-evolution without clear safety boundaries, approval requirements, or warnings about code and system changes. In an agent framework context, unattended self-modification can alter behavior, permissions, dependencies, and data handling in ways that increase the blast radius of mistakes or compromise.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The usage example shows enable_auto_evolution() as a straightforward feature toggle and states 'Skill now improves itself' without any warning, approval gate, or constraint language. This normalizes autonomous modification as a default-safe action, increasing the likelihood that operators enable persistent self-changing behavior without understanding effects on code integrity, data safety, or connected services.

Content

No source excerpt is available for this finding.

Natural-Language Policy Violations

Low
Category
Not specified by scanner
Confidence
89% confidence
Finding

The example output uses explicitly religious/cultural framing ('Dharmic Security Gates' and related terminology) without any indication that the user opted into that framing. While this is not a code-execution or data-security issue, it can create exclusion, misrepresentation, or trust concerns by presenting a value-laden worldview as a default security model.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
92% confidence
Finding

This shell script performs a network-backed pip install that modifies the user's Python environment, but it only prints a generic 'Installing dependencies...' message and does not disclose what packages are being fetched or that the command changes the local environment. For a code file, this is a safety-relevant operation and the current messaging is too minimal to count as a clear user warning.

Content

No source excerpt is available for this finding.

Missing User Warnings

Low
Category
Not specified by scanner
Confidence
81% confidence
Finding

The script creates ~/.agentic_ai/config and a skill directory under the user's home directory, which are file-system modifications. While the actions are visible in code, there is no explicit user-facing disclosure that installation will create directories in the user's home path.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.