Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add andycungkrinx91/konoha --skill assessing-vector-and-embedding-weaknessesgit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andycungkrinx91/konoha/assessing-vector-and-embedding-weaknesses)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/assessing-vector-and-embedding-weaknesses"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/assessing-vector-and-embedding-weaknesses/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/assessing-vector-and-embedding-weaknesses"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/assessing-vector-and-embedding-weaknesses.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.02724 |
| Opus 5.5 | $0.00010 | $0.01090 |
| Sonnet 5.5 | $0.00005 | $0.00545 |
| Haiku 4.5 | $0.00003 | $0.00272 |
Grade A, and why
assessing-vector-and-embedding-weaknesses scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to assessing-vector-and-embedding-weaknesses — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assessing Vector and Embedding Weaknesses
Authorized use only: These tests interact with vector stores and embedding models in RAG systems you own or are authorized to assess. Embedding inversion and cross-tenant probing against systems you do not control may expose third-party data and is prohibited without authorization.
Overview
Retrieval-Augmented Generation (RAG) systems convert documents into embedding vectors stored in a vector database (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) and retrieve the nearest vectors to ground LLM responses. OWASP LLM08:2025 Vector and Embedding Weaknesses covers the security risks unique to this layer:
- Embedding inversion — embeddings are not one-way. A trained inversion model (or a black-box reconstruction attack) can recover substantial portions of the original text from its vector, leaking source documents (maps to MITRE ATLAS AML.T0024.001 Invert ML Model).
- Membership inference — querying whether a specific record contributed to the corpus (AML.T0024.000).
- Cross-tenant / multi-tenant leakage — when one namespace/collection is shared or filter isolation is missing, a tenant retrieves another tenant's chunks.
- Knowledge-base poisoning — an attacker who can write to the corpus inserts crafted chunks that dominate retrieval (high cosine similarity to expected queries) and carry indirect prompt-injection payloads.
- Retrieval manipulation — adversarial documents tuned to be retrieved for many unrelated queries ("retrieval hijacking").
The parent technique is AML.T0024 — Exfiltration via ML Inference API: an attacker uses legitimate inference/query access to exfiltrate data (source text via inversion, membership, or model extraction). This skill provides a repeatable assessment of all five weakness classes.
When to Use
- During a security assessment of any RAG / vector-search application (OWASP LLM08 coverage).
- When a vector store is multi-tenant and you must prove namespace/metadata isolation.
- When the corpus accepts user-supplied or third-party documents (poisoning surface).
- When the embedding endpoint is externally reachable (inversion/membership surface).
- When validating retrieval-filtering controls before go-live.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 234 lines · 26 tokens per session scan A 7914de6ec106
assessing-vector-and-embedding-weaknesses is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 2,724 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to assessing-vector-and-embedding-weaknesses, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
assessing-vector-and-embedding-weaknesses
Test RAG vector stores (Pinecone, Qdrant, Weaviate, Chroma, pgvector, FAISS) for embedding inversion, cross-tenant data leakage, and data poisoning per OWASP LLM08:2025. Use when performing an authorized security assessment of a RAG pipeline's retrieval layer or auditing multi-tenant vector-store isolation.
rag-index-decisions
After making a non-obvious architectural decision, solving a novel bug, agreeing on a coding standard, or reaching a conclusion worth remembering, index it back into the knowledge base so the next occurrence is one search away. Uses adddocument or addfromurl. Closes the feedback loop that makes a RAG-backed team…
rag
Use when building grounded Q&A over your own corpus — chunk, retrieve hybrid, rerank, ground, cite chunk ids, refuse when the sources fall short — or when the right document is retrieved but the answer is still wrong, invented, or unmeasured. NOT operating the store itself — collection schema, HNSW efsearch…
rag-check-first
Before answering any technical question, code request, architecture decision, or factual claim, call searchknowledge to check the local corpus. Trigger on any query that could be answered with prior work, indexed docs, ADRs, runbooks, or team context. Prevents hallucination and forces reliance on the indexed knowledge…
rag-cite-sources
Every technical claim drawn from the local corpus must ship with a source citation formatted as path:line or path:section. Trigger whenever the response quotes, paraphrases, or acts on knowledge that came from a searchknowledge or getdocument call. Makes answers auditable and lets the user jump to source in one click.