Security checks for vulnerabilities and agentic risk
Overview
This skill is a clearly disclosed Ollama fleet-management guide, with some normal operational risks around installing an external package and managing large local models.
Install only if you trust the `ollama-herd` package and its publisher. Prefer a virtual environment, avoid administrative privileges, review the package source or pin a known version, and require explicit confirmation before any pull or delete action because models can consume or remove large amounts of disk space.
Insecure DependenciesIntroduces malicious components through unsafe dependency sources
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 (1)
T08 · Insecure Dependencies
Warning
Location
SKILL.md:22
Finding
Unpinned Third-Party Package Installation and Execution
Content
View full analysis
Vulnerability Details
File Location: SKILL.md, lines 22-26 Vulnerability Type: Unpinned and unverifiable third-party dependency Risk Level: Medium
Vulnerable Code
bash
pip install ollama-herd # install the Ollama management toolkit
herd # start the Ollama router (tracks all your Ollama machines)
herd-node # run on each Ollama machine you want to manage
Technical Analysis
The installation command retrieves the latest available ollama-herd package from the configured Python package index without specifying an exact version, package hash, lockfile, or reviewed source commit. The newly installed herd and herd-node entry points are then executed.
This creates a supply-chain trust boundary that cannot be assessed from the Skill package itself. A future package release, compromised maintainer account, compromised package-index account, or malicious package served through a modified Python index configuration could introduce arbitrary code. Python packages may execute code during installation or when their command-line entry points are launched.
The dependency requires access appropriate for managing Ollama machines and local fleet data, but installation and execution grant the package all permissions held by the invoking operating-system user. This is broader than merely making constrained requests to the documented localhost API.
The separately flagged commands at lines 38, 46, and 54 are not curl | bash execution chains. They send localhost JSON responses to python3 -m json.tool for formatting and do not dynamically execute the received content.
Attack Path
An attacker compromises the ollama-herd package publisher, release workflow, package-index account, or an index configured in the user's Python environment.
The attacker publishes a modified package under the expected package name or replaces the version se
...[truncated 1374 chars]
Remediation
View remediation
Remediation Suggestions
Pin the dependency to an explicitly reviewed release, for example:
bash
The real picture: Ollama model sizes, last-used timestamps, which machines have which Ollama models, and how much disk each is eating.
External Script Fetching
High
Category
Supply Chain
Confidence
90% confidence
Finding
Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.
Content
Scanner excerpt · SKILL.md (reported line 99)May include surrounding context.
What Ollama models should I be running?
bash
# ollama_recommendations — optimal Ollama model mix per node
curl -s http://localhost:11435/dashboard/api/recommendations | python3 -m json.tool
AI-powered Ollama recommendations based on your actual hardware — RAM, cores, GPU memory. Tells you which Ollama models fit, which are too big, and the optimal Ollama model mix for your machines. Includes estimated RAM requirements and Ollama benchmark data.
External Script Fetching
High
Category
Supply Chain
Confidence
90% confidence
Finding
Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.
Content
Scanner excerpt · SKILL.md (reported line 109)May include surrounding context.
Pull an Ollama model to a specific machine
bash
# ollama_pull — download an Ollama model to a node
curl -s -X POST http://localhost:11435/dashboard/api/pull \
-H "Content-Type: application/json" \
-d '{"model": "llama3.3:70b", "node_id": "mac-studio"}'
External Script Fetching
High
Category
Supply Chain
Confidence
90% confidence
Finding
Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.
Content
Scanner excerpt · SKILL.md (reported line 131)May include surrounding context.
Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
Content
Scanner excerpt · SKILL.md (reported line 109)May include surrounding context.
Pull an Ollama model to a specific machine
bash
# ollama_pull — download an Ollama model to a node
curl -s -X POST http://localhost:11435/dashboard/api/pull \
-H "Content-Type: application/json" \
-d '{"model": "llama3.3:70b", "node_id": "mac-studio"}'
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 · SKILL.md (reported line 131)May include surrounding context.
