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Security audit

Image Editing

Security checks for vulnerabilities and agentic risk

Overview

This skill is a straightforward image-editing guide, but users should know some examples upload selected images to third-party services.

Before using cloud examples, confirm that the image, mask, prompt, and metadata may be sent to the named provider. Prefer the documented local tools for private, regulated, customer, family, or confidential images, and install dependencies in an isolated environment with reviewed versions when possible.

A.I.G

Vulnerability Patterns
  • 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 (2)

other

Warning
Location
background-removal.md:10
Finding

Cloud Image Uploads Lack an Explicit User Consent Boundary

Content
View full analysis
Remediation
View remediation

T08 · Insecure Dependencies

Warning
Location
tools.md:89
Finding

Unpinned Third-Party Package Installation Creates Supply-Chain Exposure

Content
View full analysis
Remediation
View remediation

SkillSpector

Vulnerability Patterns
  • Data ExfiltrationExternal Transmission, Env Variable Harvesting, File System Enumeration
  • Prompt InjectionInstruction Override, Hidden Instructions, Exfiltration Commands
  • Privilege EscalationExcessive Permissions, Sudo/Root Execution, Credential Access
  • Supply ChainUnpinned Dependencies, External Script Fetching, Obfuscated Code
  • Excessive AgencyUnrestricted Tool Access, Autonomous Decision Making, Scope Creep
Findings (22)

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The skill includes examples that send local image files to third-party background-removal services, but it does not warn users that image contents are uploaded off-device and may contain sensitive or regulated data. In a general image-editing skill, this omission can lead to unintentional disclosure of personal, confidential, or customer imagery.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
86% confidence
Finding

The curl example explicitly posts photo.jpg to remove.bg, which is an external transfer of local content. While normal for a SaaS integration example, presenting it without privacy or compliance caveats can enable inadvertent leakage of sensitive image data.

Content

Scanner excerpt · background-removal.md (reported line 10)May include surrounding context.

remove.bg (API)

bash
curl -X POST "https://api.remove.bg/v1.0/removebg" \
  -H "X-Api-Key: YOUR_API_KEY" \
  -F "image_file=@photo.jpg" \
  -F "size=auto" \

External Transmission

Medium
Category
Data Exfiltration
Confidence
90% confidence
Finding

This example performs an HTTP POST to a third-party API and uploads a local image file, creating an external data transmission path. The code itself is expected for the feature, but without surrounding privacy guidance or sensitivity checks it can cause users to exfiltrate images to an external processor unintentionally.

Content

Scanner excerpt · background-removal.md (reported line 20)May include surrounding context.

python
import requests

response = requests.post(
    "https://api.remove.bg/v1.0/removebg",
    files={"image_file": open("photo.jpg", "rb")},
    data={"size": "auto"},

External Transmission

Medium
Category
Data Exfiltration
Confidence
84% confidence
Finding

This line is another occurrence of the same remove.bg upload path within the Python example and represents a real external transmission of image data. The surrounding skill context reduces suspicion of malicious intent but not the privacy risk, because users may run the snippet on sensitive files.

Content

Scanner excerpt · background-removal.md (reported line 21)May include surrounding context.

md
import requests

response = requests.post(
    "https://api.remove.bg/v1.0/removebg",
    files={"image_file": open("photo.jpg", "rb")},
    data={"size": "auto"},
    headers={"X-Api-Key": "YOUR_API_KEY"}

External Transmission

Medium
Category
Data Exfiltration
Confidence
89% confidence
Finding

This request sends photo.jpg to ClipDrop over the network, so using it on private images exposes data to an external service. In the context of an image-editing skill, that behavior is functional rather than malicious, but the lack of disclosure makes the transmission security-relevant.

Content

Scanner excerpt · background-removal.md (reported line 37)May include surrounding context.

python
import requests

response = requests.post(
    "https://clipdrop-api.co/remove-background/v1",
    files={"image_file": open("photo.jpg", "rb")},
    headers={"x-api-key": "YOUR_API_KEY"}

External Transmission

Medium
Category
Data Exfiltration
Confidence
88% confidence
Finding

This example transmits a local image to Photoroom's API, which is an external processing action with privacy implications. Because the document presents multiple cloud options without cautionary text, users may not recognize that image data leaves their environment.

Content

Scanner excerpt · background-removal.md (reported line 49)May include surrounding context.

Photoroom API

python
response = requests.post(
    "https://sdk.photoroom.com/v1/segment",
    files={"image_file": open("photo.jpg", "rb")},
    headers={"x-api-key": "YOUR_API_KEY"}

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The example sends local image and mask files to the OpenAI Images Edit API, which transfers user-provided content to an external third-party service. Without an explicit warning about data egress, users may unintentionally upload sensitive images or redacted regions they assumed would stay local, creating privacy and confidentiality risk.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
93% confidence
Finding

The skill provides example code that sends image content to OpenAI's DALL-E API but does not warn users that their images and prompts are transmitted to a third-party external service. This can expose sensitive, personal, or proprietary image data if users assume processing is local, especially in an image-editing context where uploaded files may contain private content.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

This example sends a local image file to Replicate, a third-party remote service, but does not disclose that image contents leave the local environment. In an image-editing skill, users may reasonably test with personal portraits or sensitive photos, so the omission can cause unintentional privacy and data-handling exposure.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
97% confidence
Finding

The old photo restoration snippet uploads a local photo to Replicate without clearly warning about external transmission. Old photos often contain identifiable family members or sensitive historical/personal content, so the lack of disclosure increases the chance of accidental privacy violations.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

This colorization example uses Replicate to process a local image file but does not tell the user that the file is sent externally. Because black-and-white photos are frequently personal or archival images, this can expose sensitive visual data to a third party without informed user awareness.

Content

No source excerpt is available for this finding.

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The document provides multiple examples that upload local image files to third-party cloud APIs but does not explicitly warn that user-provided images are transmitted off-device. In an image-editing skill, users may reasonably assume local processing unless clearly told otherwise, creating privacy and data-handling risk for sensitive photos, masks, or metadata.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
88% confidence
Finding

This example sends a local image file to Stability AI over the network for processing. While expected for a cloud API example, it still creates a real external data-transmission path for potentially sensitive images without any surrounding privacy warning or consent guidance.

Content

Scanner excerpt · tools.md (reported line 30)May include surrounding context.

python
import requests

response = requests.post(
    "https://api.stability.ai/v1/generation/stable-diffusion-xl-1024-v1-0/image-to-image",
    headers={"Authorization": f"Bearer {API_KEY}"},
    files={"init_image": open("image.png", "rb")},

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% 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 · tools.md (reported line 31)May include surrounding context.

md
import requests

response = requests.post(
    "https://api.stability.ai/v1/generation/stable-diffusion-xl-1024-v1-0/image-to-image",
    headers={"Authorization": f"Bearer {API_KEY}"},
    files={"init_image": open("image.png", "rb")},
    data={

External Transmission

Medium
Category
Data Exfiltration
Confidence
90% confidence
Finding

The ClipDrop background-removal example uploads a local photo to an external API. In this skill context, the transmission is functional rather than malicious, but it still exposes user images to a third party and may surprise users if not disclosed.

Content

Scanner excerpt · tools.md (reported line 48)May include surrounding context.

md
import requests

# Background removal
response = requests.post(
    "https://clipdrop-api.co/remove-background/v1",
    headers={"x-api-key": API_KEY},
    files={"image_file": open("photo.jpg", "rb")}

External Transmission

Medium
Category
Data Exfiltration
Confidence
91% confidence
Finding

This cleanup example uploads both the source image and a mask file to ClipDrop, increasing the amount of user data disclosed to the external service. Masks can reveal edited regions or user intent, so the privacy impact can exceed a simple single-image upload.

Content

Scanner excerpt · tools.md (reported line 55)May include surrounding context.

md
)

# Cleanup (remove objects)
response = requests.post(
    "https://clipdrop-api.co/cleanup/v1",
    headers={"x-api-key": API_KEY},
    files={

External Transmission

Medium
Category
Data Exfiltration
Confidence
89% confidence
Finding

The relight example transmits a local photo to ClipDrop's external API for processing. The behavior matches the skill's purpose, but absent disclosure it remains a privacy-relevant data exfiltration path from the user's environment to a third party.

Content

Scanner excerpt · tools.md (reported line 65)May include surrounding context.

md
)

# Relight
response = requests.post(
    "https://clipdrop-api.co/relight/v1",
    headers={"x-api-key": API_KEY},
    files={"image_file": open("photo.jpg", "rb")},

External Transmission

Medium
Category
Data Exfiltration
Confidence
90% confidence
Finding

The remove.bg sample uploads a local image file to an external background-removal service. This is not inherently malicious, but it is a genuine data-transfer risk because users may submit personal, proprietary, or regulated imagery without realizing it is sent off-device.

Content

Scanner excerpt · tools.md (reported line 76)May include surrounding context.

remove.bg

python
response = requests.post(
    "https://api.remove.bg/v1.0/removebg",
    headers={"X-Api-Key": API_KEY},
    files={"image_file": open("photo.jpg", "rb")},

External Transmission

Medium
Category
Data Exfiltration
Confidence
50% 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 · tools.md (reported line 77)May include surrounding context.

python
response = requests.post(
    "https://api.remove.bg/v1.0/removebg",
    headers={"X-Api-Key": API_KEY},
    files={"image_file": open("photo.jpg", "rb")},
    data={"size": "auto"}

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
94% confidence
Finding

The Magnific AI example explicitly uploads a local file (photo.jpg) to a third-party cloud API, but the documentation does not warn users that image contents will leave the local environment. This creates a real privacy and data-handling risk, especially if users process sensitive, personal, or proprietary images under the assumption that the workflow is local-only.

Content

No source excerpt is available for this finding.

External Transmission

Medium
Category
Data Exfiltration
Confidence
88% confidence
Finding

The presence of a third-party API endpoint is not inherently dangerous, but in this context it corresponds to an example that transmits user-supplied image data externally. Without disclosure or safeguards, this can expose sensitive image contents and metadata to an outside provider.

Content

Scanner excerpt · upscaling.md (reported line 43)May include surrounding context.

Magnific AI

bash
curl -X POST "https://api.magnific.ai/v1/upscale" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -F "image=@photo.jpg" \
  -F "scale=2"

Missing User Warnings

Medium
Category
Not specified by scanner
Confidence
95% confidence
Finding

The Replicate example opens a local image file and submits it to a hosted external model endpoint, but the skill provides no user-facing disclosure that the image is transmitted off-device. In an image-editing skill, users may reasonably expect some tools to be local, so omission of this distinction can lead to accidental disclosure of confidential or personal imagery.

Content

No source excerpt is available for this finding.