Western Blot Quantifier

• Outcome: Review
• Latest audit: Sep 12, 2026 · 1:04 AM UTC
• Version: 0.1.0

Overview

The skill does not appear to steal data or persist, but its documentation claims real Western blot analysis that the shipped code does not implement.

Review this skill carefully before installing. Its biggest issue is not hidden system access, but that it appears unfinished or misleading for scientific use: it does not actually analyze provided Western blot images as described. Install only in an isolated environment, pin dependencies first, and do not rely on its quantification results until the implementation and documentation are corrected and validated.

A.I.G

Vulnerability Patterns

• Insecure Dependencies: Introduces malicious components through unsafe dependency sources
• 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
• Embedded Malicious Code: Ships malicious scripts inside the skill and executes them locally

Findings (1)

T08 · Insecure Dependencies

Warning

• Location: requirements.txt:1

Finding

Unpinned Third-Party Dependencies Permit Unreviewed Supply-Chain Changes

Content

View full analysis

Vulnerability Details

File Location: requirements.txt:1-6
Related Installation Instructions: SKILL.md:112-116 and SKILL.md:196-200
Vulnerability Type: Unpinned third-party dependencies
Risk Level: Medium

Vulnerable Code

text

matplotlib
numpy
opencv-python
pandas
scikit-image
scipy

The documented installation command is:

bash

pip install -r requirements.txt

Technical Analysis

All six dependencies are specified without exact versions or integrity hashes. Consequently, each installation resolves whatever package versions are available from the configured package index at that time. The installed code can therefore differ from the dependency set that was originally developed, tested, or audited.

Python packages and their installation mechanisms may execute code with the privileges of the user performing the installation. If an upstream package release, distribution artifact, dependency account, or configured package index is compromised, following the documented installation procedure could introduce attacker-controlled code. The absence of hashes also prevents pip from verifying that downloaded artifacts match a previously reviewed set.

This finding does not establish that any currently named package is malicious. It identifies a reproducibility and supply-chain control weakness that makes future installations dependent on mutable, unverified artifacts.

Attack Path

1. An attacker compromises an upstream dependency release, its publishing account, its distribution artifact, or a package index used by the installer.
2. The attacker publishes a malicious version under one of the dependency names listed in requirements.txt, or causes the configured index to resolve an attacker-controlled artifact.
3. A user follows the documented command pip install -r requirements.txt.
4. Because no exact versions or hashes are enforced, pip resolves and downloads ...[truncated 860 chars]

Remediation

View remediation

Remediation Suggestions

1. Pin every direct dependency to an exact, reviewed version using ==.
2. Generate and commit a deterministic lock file that includes resolved transitive dependencies.
3. Record cryptographic hashes for all approved artifacts and install with pip install --require-hashes -r requirements.txt.
4. Obtain packages only from an explicitly configured, trusted index; avoid dependency resolution against untrusted or mixed public/private sources.
5. Run dependency vulnerability and provenance checks in CI before accepting lock-file updates.
6. Review dependency updates through controlled pull requests and test them before deployment.
7. Install and execute the skill in an isolated virtual environment or sandbox under a non-privileged account with restricted filesystem and network access.
8. Update the installation documentation in SKILL.md to reference the locked, hash-verified dependency file.

SkillSpector

Vulnerability Patterns

• Data Exfiltration: External Transmission, Env Variable Harvesting, File System Enumeration
• Supply Chain: Unpinned Dependencies, External Script Fetching, Obfuscated Code
• MCP Tool Poisoning: Hidden Instructions, Unicode Deception, Parameter Description Injection
• Prompt Injection: Instruction Override, Hidden Instructions, Exfiltration Commands
• Privilege Escalation: Excessive Permissions, Sudo/Root Execution, Credential Access

Findings (14)

Tp4

High

• Category: MCP Tool Poisoning
• Confidence: 94% confidence

Finding

The skill documentation claims automated Western blot quantification, densitometry, and normalization to loading controls, but the described behavior mismatch indicates the implementation may not read the actual image correctly, may use simplistic peak heuristics instead of true densitometric analysis, and may normalize against the wrong control signal. In a scientific analysis context, this can silently produce materially incorrect results that users may trust for research conclusions, making it an integrity and reproducibility risk rather than a classic code-execution issue.

Content

No source excerpt is available for this finding.

Ae4

Medium

• Category: analysis-evasion
• Confidence: 80% confidence

Finding

Suspicious Unicode normalization or mixed-script content

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium

• Category: Not specified by scanner
• Confidence: 95% confidence

Finding

The code does not perform true densitometric analysis or normalization to loading controls as described. It simply divides each band's intensity by the first detected band in a selected lane, which can produce scientifically misleading results and cause users to trust invalid quantitative conclusions.

Content

No source excerpt is available for this finding.

Description-Behavior Mismatch

Medium

• Category: Not specified by scanner
• Confidence: 98% confidence

Finding

The tool advertises automatic image-based Western blot analysis, but the executable path never reads or analyzes the user-supplied --image input and only operates on synthetic demo data. This creates a deceptive mismatch between claimed and actual behavior, which may lead users to rely on nonexistent analysis and make incorrect research or reporting decisions.

Content

No source excerpt is available for this finding.

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 97% confidence

Finding

The dependency list leaves matplotlib unpinned, which makes builds non-reproducible and can allow a future malicious, compromised, or breaking release to be installed unexpectedly. While this file alone does not prove exploitation, unpinned packages are a real supply-chain hygiene weakness because security posture depends on whatever version resolves at install time.

Content

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

text

matplotlib
numpy
opencv-python
pandas

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 98% confidence

Finding

numpy is unpinned, so the installed version is uncontrolled and may vary across environments or over time. This creates supply-chain risk and prevents verification that the resolved version is free from known vulnerabilities or incompatible changes.

Content

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

text

matplotlib
numpy
opencv-python
pandas
scikit-image

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: 95% confidence

Finding

numpy has known advisories, and because no version is pinned, it is impossible to verify whether the installed release is affected. The danger is contextual rather than proof of compromise: unresolved version ambiguity can silently expose consumers to vulnerable builds.

Content

No source excerpt is available for this finding.

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 98% confidence

Finding

opencv-python is unpinned, meaning deployments may pull different releases with different security properties. For a package that processes complex image inputs, uncontrolled version selection increases exposure to parser and memory-safety issues in underlying native code.

Content

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

text

matplotlib
numpy
opencv-python
pandas
scikit-image
scipy

Unverifiable Dependency: opencv-python has 16 known advisory(ies) (CVE-2017-12864 (Integer Overflow or Wraparound in OpenCV); CVE-2017-12598 (Out-of-bounds Read in OpenCV ); CVE-2019-14493 (NULL Pointer Dereference in OpenCV.) +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: 97% confidence

Finding

opencv-python has multiple known advisories, and the absence of a version pin means the environment could resolve to an affected release without visibility. In this skill's context, the package likely handles user-supplied image files, which makes image-decoding vulnerabilities more relevant than in a non-image-processing project.

Content

No source excerpt is available for this finding.

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 96% confidence

Finding

pandas is unpinned, so dependency resolution may install an arbitrary future or outdated release. That weakens reproducibility and makes it difficult to assure that the package version in use does not contain known defects or security issues.

Content

Scanner excerpt · requirements.txt (reported line 4)May include surrounding context.

text

matplotlib
numpy
opencv-python
pandas
scikit-image
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: 88% confidence

Finding

pandas has at least one advisory, and without version pinning there is no way to determine if installations are exposed. The skill description does not strongly suggest risky pandas features like unsafe deserialization are used, so the contextual danger appears limited but still represents poor dependency assurance.

Content

No source excerpt is available for this finding.

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 96% confidence

Finding

scikit-image is unpinned, which permits non-deterministic installs and increases supply-chain uncertainty. In an image-processing skill, this matters because image parsing libraries can be affected by input-triggered vulnerabilities, and version ambiguity prevents meaningful risk verification.

Content

Scanner excerpt · requirements.txt (reported line 5)May include surrounding context.

text

numpy
opencv-python
pandas
scikit-image
scipy

Unpinned Dependencies

Low

• Category: Supply Chain
• Confidence: 96% confidence

Finding

scipy is unpinned, so the runtime may receive any resolver-selected release, including versions with known issues or incompatible behavior. This is a real but low-severity supply-chain control weakness rather than an immediately exploitable flaw by itself.

Content

Scanner excerpt · requirements.txt (reported line 6)May include surrounding context.

text

opencv-python
pandas
scikit-image
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 the missing version pin prevents verifying whether deployed versions are vulnerable. This is primarily a dependency governance issue; exploitability depends on whether affected components are actually used, which is not shown here.

Content

No source excerpt is available for this finding.