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Security audit

Qr Code Toolkit

Security checks for vulnerabilities and agentic risk

Overview

This is a mostly coherent local QR/barcode toolkit, but its batch generator can write files outside the chosen output folder if a CSV or JSON file contains unsafe filenames.

Use this skill only with trusted CSV/JSON batch files, and inspect filename fields before running batch generation. Install it in an isolated Python environment, pin or review dependency versions, and treat WiFi/contact QR images and decoded JSON outputs as sensitive files.

SkillSpector

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
  • MCP Least PrivilegeUnderdeclared Capability, Wildcard Permission, Missing Permission Declaration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
Findings (12)

Lp3

Medium
Category
MCP Least Privilege
Confidence
84% confidence
Finding
The skill documentation advertises scripts that read input files and write output images or batch results, but no permissions are declared to reflect those capabilities. This creates a transparency and governance gap: users or platforms may authorize the skill without understanding that it can access local files and persist generated or decoded data to disk.

Context-Inappropriate Capability

Medium
Confidence
97% confidence
Finding
The CSV path uses a user-controlled filename field and joins it directly with the requested output directory. An attacker can supply values like '../outside.png' or absolute paths to write generated files outside the intended directory, potentially overwriting arbitrary files that the process has permission to modify.

Context-Inappropriate Capability

Medium
Confidence
97% confidence
Finding
The JSON batch flow has the same issue: each item's filename is taken from untrusted input and combined with output_dir without normalization or containment checks. This enables path traversal or arbitrary file write within the privileges of the running user, which is especially risky in automation or shared environments.

Missing User Warnings

Medium
Confidence
92% confidence
Finding
The WiFi QR example embeds an SSID and password directly into a generated image file, but the documentation does not warn that this output contains reusable network credentials and may be stored, shared, synced, or logged inadvertently. In this skill's context, generating credential-bearing QR codes is a core feature, which makes omission of handling guidance more dangerous because users are likely to treat the image as harmless media.

Unpinned Dependencies

Low
Category
Supply Chain
Content
qrcode>=7.4.2
pillow>=10.0.0
opencv-python>=4.8.0
pyzbar>=0.1.9
Confidence
92% confidence
Finding
The dependency is specified with a lower-bound range rather than an exact version, which makes builds non-reproducible and can unexpectedly pull in newer releases with breaking changes or newly introduced supply-chain risk. In a security-sensitive packaging context, unpinned dependencies are a real weakness even if they are common practice and not inherently malicious.

Unpinned Dependencies

Low
Category
Supply Chain
Content
qrcode>=7.4.2
pillow>=10.0.0
opencv-python>=4.8.0
pyzbar>=0.1.9
python-barcode>=0.15.1
Confidence
95% confidence
Finding
Using pillow>=10.0.0 allows any future Pillow release to be installed, reducing reproducibility and increasing exposure to dependency confusion, malicious upstream compromise, or accidental adoption of vulnerable versions. This is especially relevant because the skill processes attacker-controlled image content.

Unpinned Dependencies

Low
Category
Supply Chain
Content
qrcode>=7.4.2
pillow>=10.0.0
opencv-python>=4.8.0
pyzbar>=0.1.9
python-barcode>=0.15.1
numpy>=1.24.0
Confidence
95% confidence
Finding
opencv-python>=4.8.0 permits unbounded upgrades, which can introduce vulnerable or incompatible releases without notice. Because OpenCV often parses complex image formats, controlling the exact vetted version is important for limiting exposure.

Unpinned Dependencies

Low
Category
Supply Chain
Content
qrcode>=7.4.2
pillow>=10.0.0
opencv-python>=4.8.0
pyzbar>=0.1.9
python-barcode>=0.15.1
numpy>=1.24.0
Confidence
90% confidence
Finding
pyzbar is left unpinned, so installations may resolve to different versions over time, undermining reproducibility and increasing supply-chain uncertainty. While lower severity than direct code issues, this is still a genuine dependency hygiene problem.

Unpinned Dependencies

Low
Category
Supply Chain
Content
pillow>=10.0.0
opencv-python>=4.8.0
pyzbar>=0.1.9
python-barcode>=0.15.1
numpy>=1.24.0
Confidence
90% confidence
Finding
python-barcode>=0.15.1 allows any later version, creating avoidable supply-chain and stability risk. Even if no current exploit is evident, exact pinning is preferred for secure, reproducible builds.

Unpinned Dependencies

Low
Category
Supply Chain
Content
opencv-python>=4.8.0
pyzbar>=0.1.9
python-barcode>=0.15.1
numpy>=1.24.0
Confidence
92% confidence
Finding
numpy>=1.24.0 is unpinned, so future installs may pull in unexpected releases with security regressions or behavioral changes. Given that NumPy is a foundational native-extension package, deterministic version control is important.

Known Vulnerable Dependency: pillow — 10 advisory(ies): CVE-2016-2533 (Pillow buffer overflow in ImagingPcdDecode); CVE-2023-50447 (Arbitrary Code Execution in Pillow); CVE-2021-27922 (Pillow Uncontrolled Resource Consumption) +7 more

Critical
Category
Supply Chain
Confidence
82% confidence
Finding
The finding is directionally valid because the requirement does not pin Pillow to a specifically patched release, and Pillow has a history of image-parsing vulnerabilities including memory corruption and resource exhaustion. In this skill, Pillow is used in a QR/barcode tool that likely handles untrusted uploaded images, which increases the practical risk of exploitation or denial of service if an affected version is installed.

Known Vulnerable Dependency: opencv-python — 10 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.) +7 more

High
Category
Supply Chain
Confidence
84% confidence
Finding
The package constraint allows potentially affected OpenCV builds, and OpenCV has a long history of vulnerabilities in image decoding and native code paths. Because this skill explicitly decodes QR codes/barcodes from external images, the context makes parser flaws more dangerous than in a non-image-processing package.

Static analysis

No suspicious patterns detected.