Natural language interface to OpenCTI threat intelligence. Built with Claude Code for $22. Part of Cooper Cyber Coffee.
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Opencti_mcp_server is an MCP server that natural language interface to OpenCTI threat intelligence. Built with Claude Code for $22. Part of Cooper Cyber Coffee. Its tool list has not been published yet over stdio and sse, requires no API key, and scores 54/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"opencti": {
"env": {
"OPENCTI_URL": "http://localhost:8080",
"OPENCTI_TOKEN": "your-api-token-here",
"OPENCTI_SSL_VERIFY": "false"
},
"args": [
"-m",
"opencti_mcp"
],
"command": "/full/path/to/venv/bin/python"
}
}
}Are you the author?
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Connect Claude Desktop to OpenCTI for AI-augmented threat intelligence analysis
This server supports HTTP transport. Be the first to test it - help the community know if it works.
Five weighted categories - click any category to see the underlying evidence.
vLLM: Request-selected PyNvVideoCodec GPU decode bypasses static VRAM reservation
## Summary Current vLLM `main` lets an inference request choose the PyNvVideoCodec GPU video decoder through `media_io_kwargs.video.video_backend`, but engine GPU memory reservation is computed only from static startup configuration and `VLLM_VIDEO_LOADER_BACKEND`. If the server starts with the default OpenCV/software backend and no `--mm-ipc-gpu-memory-gb` budget, a client can still route a video request into the PyNvVideoCodec path after startup, causing frontend CUDA-context, decoder-surface
vLLM: Unauthenticated audio decompression-bomb DoS in /v1/chat/completions
### Summary The audio decode-duration guard (`max_duration_s`, env `VLLM_MAX_AUDIO_DECODE_DURATION_S`, default 600s) that protects against audio decompression-bomb DoS is wired into **only** the speech-to-text path (`/v1/audio/transcriptions`). The **chat** audio path (`/v1/chat/completions`, `input_audio` content parts) calls the same decoder with **no** limit, so an **unauthenticated** client can submit a few-KB compressed audio file that expands to multiple GB of float32 PCM at decode time, O
PYSEC-2026-3985
vLLM before 0.28.0 contains a remote code execution vulnerability in the LlavaOnevision2 processor loader that ignores the trust_remote_code parameter when loading remote processor classes. Attackers can craft a malicious model with arbitrary code in processing_llava_onevision2.py that executes with vLLM process authority even when trust_remote_code is set to False.
vLLM: SSRF + arbitrary local file read in MiMoV2OmniMultiModalProcessor `_fetch_image` and audio loader bypass MediaConnector protections
### Summary `vllm/transformers_utils/processors/mimo_v2_omni.py` — the multimodal processor for `MiMoV2OmniForCausalLM` — issues `requests.get(...)` directly on user-supplied image and audio URL strings and `Image.open(...)` on user-supplied local paths, **without** the SSRF / `allowed_local_media_path` checks that `vllm.multimodal.utils.MediaConnector` was hardened with in **GHSA-qh4c-xf7m-gxfc**, **GHSA-v359-jj2v-j536**, and **GHSA-pf3h-qjgv-vcpr**. This is the same bug class as those three
vLLM: Cross-User Data Leak Vulnerability
### Summary An integer overflow in the act_and_mul_kernel kernel can cause the output of one user request to be incorporated into the response of another request within the same inference batch. Under certain conditions, the last request in a batch can receive a partial or complete copy of the first user's inference result, resulting in cross-user data leakage. ### Details The root cause is an integer overflow in the expression blockIdx.x * 2 * d at https://github.com/vllm-project/vllm/blob/ff
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