Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"ogham-mcp": {
"args": [
"-y",
"skills"
],
"command": "npx"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
Ogham (pronounced "OH-um") -- persistent, searchable shared memory for AI coding agents. Works across clients.
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.
LiteLLM has a sandbox escape in custom-code guardrail
### Impact The `POST /guardrails/test_custom_code` endpoint runs user-supplied Python inside a hand-rolled sandbox. The sandbox can be escaped using bytecode-level techniques, allowing arbitrary code execution in the proxy process — which runs as root in the default Docker image. **Reaching the endpoint requires a proxy-admin credential** in default configurations. ### Patches Fixed in **`1.83.11`**. The hand-rolled sandbox has been replaced with `RestrictedPython`. Upgrade to `1.83.11` or l
LiteLLM: Authenticated command execution via MCP stdio test endpoints
### Impact Two endpoints used to preview an MCP server before saving it — `POST /mcp-rest/test/connection` and `POST /mcp-rest/test/tools/list` — accepted a full server configuration in the request body, including the `command`, `args`, and `env` fields used by the stdio transport. When called with a stdio configuration, the endpoints attempted to connect, which spawned the supplied command as a subprocess on the proxy host with the privileges of the proxy process. The endpoints were gated onl
LiteLLM has SQL Injection in Proxy API key verification
### Impact A database query used during proxy API key checks mixed the caller-supplied key value into the query text instead of passing it as a separate parameter. An unauthenticated attacker could send a specially crafted `Authorization` header to any LLM API route (for example `POST /chat/completions`) and reach this query through the proxy's error-handling path. An attacker could read data from the proxy's database and may be able to modify it, leading to unauthorised access to the proxy an
LiteLLM: Server-Side Template Injection in /prompts/test endpoint
### Impact The `POST /prompts/test` endpoint accepted user-supplied prompt templates and rendered them without sandboxing. A crafted template could run arbitrary code inside the LiteLLM Proxy process. The endpoint only checks that the caller presents a valid proxy API key, so any authenticated user could reach it. Depending on how the proxy is deployed, this could expose secrets in the process environment (such as provider API keys or database credentials) and allow commands to be run on the ho
LiteLLM: Server-Side Template Injection in /prompts/test endpoint
### Impact The `POST /prompts/test` endpoint accepted user-supplied prompt templates and rendered them without sandboxing. A crafted template could run arbitrary code inside the LiteLLM Proxy process. The endpoint only checks that the caller presents a valid proxy API key, so any authenticated user could reach it. Depending on how the proxy is deployed, this could expose secrets in the process environment (such as provider API keys or database credentials) and allow commands to be run on the ho
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in productivity / ai-ml
Persistent memory using a knowledge graph
Privacy-first. MCP is the protocol for tool access. We're the virtualization layer for context.
Official Miro MCP server - Supports context to code and creating diagrams, docs, and data tables.
An open-source AI agent that brings the power of Gemini directly into your terminal.
MCP Security Weekly
Get CVE alerts and security updates for Ogham Mcp and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
Ogham (pronounced "OH-um") -- persistent, searchable shared memory for AI coding agents. Works across clients.
85.8% QA accuracy on the AMB benchmark harness (500 questions, April 2026) -- 429/500 questions answered correctly using GPT-5-mini with reasoning, evaluated by Gemini 2.5 Flash Lite as a strict judge. Retrieval R@10: 99.5%. AMB is the standardised evaluation harness built by the Vectorize team (creators of Hindsight). Thanks to Nicolo and the Vectorize team for making the harness open.
Previously: 91.8% on our internal LongMemEval benchmark pipeline (gpt-5.4-mini reader, rubric judge). The AMB number is lower because AMB uses a stricter substring-matching judge -- see the full write-up for methodology differences.
0.554 nugget score on BEAM 100K (400 questions across 10 memory abilities, ICLR 2026), using the paper's exact judge prompt from Appendix G. The published baseline is 0.358 (Llama-4-Maverick + LIGHT). Retrieval R@10: 0.737. Seven of nine categories beat the paper. Full write-up.
End-to-end QA accuracy on LongMemEval (retrieval + LLM reads and answers):
| System | Accuracy | Architecture |
|---|---|---|
| OMEGA | 95.4% | Classification + extraction pipeline |
| Observational Memory (Mastra) | 94.9% | Observation extraction + GPT-5-mini |
| Ogham v0.9.2 | 85.8% | Verbatim + read-time extraction + gpt-5-mini (AMB harness, strict judge) |
| Ogham v0.9.1 | 91.8% | Hybrid search + context engineering + gpt-5.4-mini (internal benchmark) |
| Hindsight (Vectorize) | 91.4% | 4 memory types + Gemini-3 |
| Zep (Graphiti) | 71.2% | Temporal knowledge graph + GPT-4o |
| Mem0 | 49.0% | RAG-based |
Retrieval only (R@10 -- no LLM in the search loop):
| System | R@10 | Architecture |
|---|---|---|
| Ogham |