Table 1 Generator

• Outcome: Pass
• Latest audit: Sep 16, 2026 · 4:33 AM UTC
• Version: 1.0.0

Overview

This skill is a straightforward local clinical table generator, with manageable dependency and CSV-output cautions but no hidden or purpose-mismatched behavior.

Install and run this in a virtual environment, consider pinning dependency versions before use, and avoid opening generated CSVs from untrusted datasets in spreadsheet software unless formula-like cells have been neutralized. Because this handles clinical research data, keep input and output files in an appropriate protected workspace.

A.I.G

Vulnerability Patterns

• Insecure Dependencies: Introduces malicious components through unsafe dependency sources
• Insecure Skill Coding Practices: Finds exploitable flaws such as hardcoded secrets or command injection
• Skill Instruction Hijacking: Alters the agent's session goals or safety constraints when the skill loads
• Agent Memory Poisoning: Writes attacker-controlled rules into memory that affect later sessions
• Remote Payload Retrieval and Execution: Fetches external code whose behavior can change after review

Findings (2)

T08 · Insecure Dependencies

Warning

• Location: requirements.txt:1

Finding

Unpinned Third-Party Dependencies Create a Supply-Chain Risk

Content

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Remediation

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pandas== scipy== ``` 2. Generate a lock file that includes all transitive dependencies. 3. Record cryptographic hashes and install with hash verification: ```bash python -m pip install --require-hashes -r requirements.txt ``` 4. Use a controlled package index or approved internal mirror. 5. Integrate dependency vulnerability and provenance scanning into release workflows. 6. Regularly update pinned versions through a reviewed process rather than allowing installations to resolve automatically to the newest available releases. 7. Install and run the project in a least-privileged virtual environment or container. ]]>

T09 · Insecure Skill Coding Practices

Warning

• Location: scripts/main.py:178

Finding

Attacker-Controlled CSV Content Can Reach Spreadsheet Cells Without Formula Neutralization

Content

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Remediation

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SkillSpector

Vulnerability Patterns

• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• 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
• Excessive Agency: Unrestricted Tool Access, Autonomous Decision Making, Scope Creep

Findings (6)

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 97% confidence

Finding

The dependency on numpy is unpinned, so installations may resolve to different versions over time, reducing build reproducibility and potentially pulling in a vulnerable or breaking release. In a clinical-research automation skill, this is mainly a supply-chain hygiene issue rather than an immediate exploit path, but it still increases risk because results and runtime behavior can change unexpectedly.

Content

Scanner excerpt · requirements.txt (reported line 1)May include surrounding context.

text

numpy
pandas
scipy

Unverifiable Dependency: numpy has 16 known 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)) +13 more), but the manifest does not pin a version, so it is unknown whether the installed release is affected

Low

• Category: Supply Chain
• Confidence: 92% confidence

Finding

Numpy has known advisories, and because no version is pinned, there is no way to verify whether deployments will install a safe or affected release. This is dangerous because environments may resolve to different versions, leaving downstream users unknowingly exposed to historical or future vulnerabilities.

Content

No source excerpt is available for this finding.

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 97% confidence

Finding

The dependency on pandas is unpinned, which allows future installs to pick arbitrary newer or older compatible releases. This creates supply-chain and reproducibility risk, especially for data-processing code used to generate research tables where silent behavior changes could affect outputs.

Content

Scanner excerpt · requirements.txt (reported line 2)May include surrounding context.

text

numpy
pandas
scipy

Unverifiable Dependency: pandas has 1 known advisory(ies) (CVE-2020-13091 (** DISPUTED ** pandas through 1.0.3 can unserialize and execute commands from an)), but the manifest does not pin a version, so it is unknown whether the installed release is affected

Low

• Category: Supply Chain
• Confidence: 89% confidence

Finding

Pandas has at least one known advisory, and the absence of version pinning means the installed package cannot be verified as patched or unaffected. In a data-analysis skill, this is mostly a dependency governance issue, but it still creates avoidable uncertainty around the security posture of deployed environments.

Content

No source excerpt is available for this finding.

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 97% confidence

Finding

The dependency on scipy is unpinned, making builds non-reproducible and exposing consumers to unexpected upstream changes or vulnerable releases. While this file alone does not prove exploitation, it is a real weakness in dependency management and increases supply-chain exposure.

Content

Scanner excerpt · requirements.txt (reported line 3)May include surrounding context.

text

numpy
pandas
scipy

Unverifiable Dependency: scipy has 4 known 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), but the manifest does not pin a version, so it is unknown whether the installed release is affected

Low

• Category: Supply Chain
• Confidence: 91% confidence

Finding

Scipy has known advisories, and without a pinned version the manifest cannot establish whether installations are safe. This weakens supply-chain assurance and can lead to inconsistent deployments across systems, even if no direct exploit is evident from this file alone.

Content

No source excerpt is available for this finding.

Static analysis

No suspicious patterns detected.