dsh-vision-toolkit
[dsh]为纯文本模型设计更强大的视觉工具箱：一行安装使用、粘贴图片直接识别、多张图片问答、截图到前端UI 还原等｜DeepSeek Harness-native integration for agent-vision-toolkit: image Q&A, long-screenshot OCR, UI restoration, grounding, pixel diff, Artifacts, and Web UI.
No outside reports on file
Use this toolkit only with images you control and trust; be cautious with sensitive screenshots or documents, and verify where image data is being processed.
Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee.
What it can access
- Local filesReads & writes local foldersREVIEW
- Command executionNo shell command runner notedLOW
- Network reachLocal or standard API callsLOW
- CredentialsRequires API key or tokenREVIEW
- Package sourceGitHub (Anionex/dsh-vision-toolkit)PUBLIC
Inferred from this listing’s public package details and description. Check the project’s source before installing.
Install
No npm or PyPI package is listed. Install from source: github.com/Anionex/dsh-vision-toolkit ↗
Everything known
We found nothing about this project in the sources we read (last checked 2026-10-30). That does not mean it has no problems.
0 incidents · 0 advisories · 0 news articles · 0 community threads · checked 2026-10-30
Popularity grade: C (from public signals such as stars and downloads, not a code review). Graded 2026-10-23.
Sources we read: OSV, GitHub Advisory Database, CISA Known Exploited Vulnerabilities, 44 security news feeds and our incident library. Last checked 2026-10-30.
About Dsh Vision Toolkit
This toolkit adds vision (image understanding) abilities to text-only AI models like DeepSeek. It can read images, answer questions about multiple images, extract text from screenshots, identify UI elements, and spot differences between images.
Who it’s for
Developers building AI agents or chatbots that need to process and understand images. Teams using DeepSeek models who want vision features without switching to multimodal models.
Alternatives
- Claude's native vision API (built into Claude models; no separate install needed)
- OpenAI Vision (multimodal GPT models with image understanding)
- LM Studio with vision models (local, offline image processing)
- LLaVA or similar open-source multimodal models (run locally, privacy-focused)
Setup
Check these steps against the project’s own instructions before running anything.
Follow the project's README for installation and integration steps specific to your setup.
Why this grade
Signal-based grade (3/7 points): well starred on GitHub, public source code. This reflects adoption and transparency, not a code review.
Popularity and upkeep
- GitHub stars
- 915
- Listing last refreshed
- 2026-10-30
These are public popularity signals, not a maintenance check. Look at the repository’s commit history and open issues.
Risks to watch for
- Image data exposure: Images you feed it may be sent to external AI services or logged, potentially leaking sensitive screenshots, documents, or personal photos.
- Misleading visuals: An attacker could craft images with hidden text, fake UI elements, or visual tricks designed to make the AI misunderstand and act incorrectly.
- OCR unreliability: Extracted text from screenshots could be misread, causing the AI to misinterpret credentials, codes, or instructions embedded in images.
- UI restoration trust: If the tool rebuilds UIs from screenshots, malicious pages could be recreated to deceive the AI into performing unintended actions.
- File access risk: Depending on setup, the tool may access local files or take screenshots from your system, which could capture private information.
How to lower the risk
Least-privilege permissions
- Only grant image access to directories you need scanned.
- Avoid running with full system screenshot access unless necessary.
Network isolation
- Route image processing through controlled services you manage if possible.
- Verify the toolkit doesn't send raw images to untrusted external APIs.
Monitoring
- Log which images are processed and where they're sent.
- Track any API calls the toolkit makes to external services.
Red flags to watch for
- Images that produce unexpected AI responses or seem to override instructions.
- Logs showing images sent to unfamiliar or third-party services.
- Performance changes that suggest hidden processing or data exfiltration.
Questions
How do I install Dsh Vision Toolkit?
Dsh Vision Toolkit does not publish a standalone npm or PyPI package. Clone or follow the setup instructions in its source repository at github.com/Anionex/dsh-vision-toolkit.
Are there security reports about Dsh Vision Toolkit?
No reports found for this package in our research (last checked 2026-10-30). Dsh Vision Toolkit currently holds Grade C (Moderate risk signals) based on public adoption and transparency signals. Key risk areas to watch include image data exposure, misleading visuals, ocr unreliability.
Is Dsh Vision Toolkit actively maintained?
Dsh Vision Toolkit has 915 GitHub stars. Our directory data was last refreshed on 2026-10-30. Check recent commit activity, release tags, and open issues at github.com/Anionex/dsh-vision-toolkit before pinning it in production.
What are alternatives to Dsh Vision Toolkit?
Notable alternatives to Dsh Vision Toolkit include Claude's native vision API, OpenAI Vision, LM Studio with vision models, LLaVA or similar open-source multimodal models. You can also compare Dsh Vision Toolkit side by side with other skills in the Related tools section on this page.
README badge and team notes
Maintain or use Dsh Vision Toolkit? Show its live grade in your GitHub README, docs site, or run a check from the terminal before you install.
GitHub README Markdown:
[](https://agentarmorhq.com/skills/anionex-dsh-vision-toolkit)HTML embed:
<a href="https://agentarmorhq.com/skills/anionex-dsh-vision-toolkit"><img src="https://agentarmorhq.com/api/badge/anionex-dsh-vision-toolkit" alt="AgentArmorHQ Risk Grade for Dsh Vision Toolkit" height="20" /></a>Terminal check:
curl -fsSL https://agentarmorhq.com/api/cli | node --input-type=module - check "anionex-dsh-vision-toolkit"- Compare Dsh Vision Toolkit →
- Add to a team allowlist
- Claude and terminal guide →
- Download the PDF guide
Share with your security lead
Need sign-off to use dsh-vision-toolkit? Copy this note for Slack, a ticket or a pull request.
### AgentArmorHQ Security Review Memo: dsh-vision-toolkit
- **Listing URL:** https://agentarmorhq.com/skills/anionex-dsh-vision-toolkit
- **Type:** AI agent skill
- **Risk Grade:** Grade C (Moderate risk signals)
- **Outside Security Advisories on File:** 0
- **Public Adoption:** 915 GitHub stars
#### Permission & Blast-Radius Signals
- **Local files:** Reads & writes local folders
- **Command execution:** No shell command runner noted
- **Network reach:** Local or standard API calls
- **Credentials:** Requires API key or token
- **Package source:** GitHub (Anionex/dsh-vision-toolkit)
#### Recommended Hardened Setup
- **Source Repo:** https://github.com/Anionex/dsh-vision-toolkit
- **Controls:** Pin an exact version in `.mcp.json`, scope access to a single workspace folder, and use a least-privilege read-only token where supported.
_Based on public adoption and transparency signals as of Oct 23, 2026. Our opinion, not a code audit or security guarantee._Key facts
- Type
- AI agent skill
- Publisher
- Anionex
- Source code
- github.com/Anionex/dsh-vision-toolkit
- Est. context load
- ~82 tokens idle · ~450 tokens when active (on-demand skill)
- Popularity
- 915 GitHub stars
- Last updated
- 2026-10-30
- Last graded
- 2026-10-23
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