Pre-clinical PK/PD Analyst

• Outcome: Pass
• Latest audit: May 29, 2026 · 8:32 PM UTC
• Version: 1.0.0

Overview

This is a local PK/PD analysis helper with ordinary math dependencies and no evidence of hidden access, persistence, network use, or data theft.

Install in a virtual environment, pin and audit numpy/scipy versions if reproducibility matters, and only run the script on trusted PK/PD datasets with user-confirmed input paths. Treat outputs as analytical support that still need scientific review before use in regulatory or dosing decisions.

SkillSpector

By NVIDIA

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• Trigger Abuse: Overly Broad Trigger, Shadow Command Trigger, Keyword Baiting Trigger
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access

Findings (6)

Vague Triggers

Medium

• Confidence: 88% confidence
• Finding: The invocation guidance is broad enough that an agent could select this skill for generic data-analysis requests without strong exclusion criteria, increasing the chance of unnecessary local code execution and file access. In a skill that packages a Python script and encourages editing config and running it, ambiguous scope boundaries can cause unsafe or unintended use on untrusted inputs or inappropriate datasets.

Missing User Warnings

Medium

• Confidence: 91% confidence
• Finding: The skill instructs users to run a local Python script and later acknowledges reading input files and writing outputs, but it does not prominently warn about these side effects before use. This can lead operators to invoke the skill without understanding that it may execute code and modify the workspace, which is especially risky when inputs or paths are user-controlled.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

numpy
scipy

• Confidence: 98% confidence
• Finding: The dependency 'numpy' is unpinned, so installs may resolve to different versions over time, harming reproducibility and potentially pulling in a newly introduced vulnerable or incompatible release. In a data-analysis skill, this increases supply-chain and reliability risk because package resolution is not controlled.

Unpinned Dependencies

Low

• Category: Supply Chain

Content

numpy
scipy

• Confidence: 98% confidence
• Finding: The dependency 'scipy' is also unpinned, which allows uncontrolled version drift during installation. This can introduce vulnerable releases or break the skill unexpectedly, especially in environments where builds are repeated over time.

Known Vulnerable Dependency: numpy — 10 advisory(ies): CVE-2014-1859 (Numpy arbitrary file write via symlink attack); CVE-2021-41495 (NumPy NULL Pointer Dereference); CVE-2021-33430 (NumPy Buffer Overflow (Disputed)) +7 more

Critical

• Category: Supply Chain
• Confidence: 90% confidence
• Finding: The file requests 'numpy' without a version, and static analysis associates the package with multiple historical advisories. While the requirements file does not prove a vulnerable version will be installed, the absence of pinning means a susceptible version could be resolved in some environments, making this a real supply-chain exposure rather than a purely theoretical issue.

Known Vulnerable Dependency: scipy — 4 advisory(ies): CVE-2013-4251 (SciPy creates insecure temporary directories); CVE-2013-4251 (The scipy.weave component in SciPy before 0.12.1 creates insecure temporary dire); CVE-2023-25399 (A refcounting issue which leads to potential memory leak was discovered in scipy) +1 more

High

• Category: Supply Chain
• Confidence: 90% confidence
• Finding: The dependency 'scipy' has known advisories, and because no version is specified, the installation process may select a vulnerable release depending on environment and resolver behavior. For an analytical workflow skill, this raises the risk of insecure temporary file handling, memory-safety problems, or other library-level weaknesses in the execution environment.

VirusTotal

65/65 vendors flagged this skill as clean.

Static analysis

No suspicious patterns detected.