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 Youngmaidainon/Agent-Level-Up --skill assessing-vector-and-embedding-weaknessesgit clone --depth 1 https://github.com/Youngmaidainon/Agent-Level-UpWrote 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/youngmaidainon/agent-level-up/assessing-vector-and-embedding-weaknesses)<a href="https://agentmods.dev/skills/youngmaidainon/agent-level-up/assessing-vector-and-embedding-weaknesses"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/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/youngmaidainon/agent-level-up/assessing-vector-and-embedding-weaknesses"><img src="https://agentmods.dev/badge/skills/youngmaidainon/agent-level-up/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 | $0.00085 | $0.02792 |
| Opus 5 | $0.00043 | $0.01396 |
| Sonnet 5 | $0.00017 | $0.00558 |
| Haiku 4.5 | $0.00009 | $0.00279 |
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 8d ago.
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
91% identical to assessing-vector-and-embedding-weaknesses — 9 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 — 237 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.
- 8d ago First seen · 237 lines · 85 tokens per session scan A 645db88420e8
assessing-vector-and-embedding-weaknesses is a skill published in the GitHub repository Youngmaidainon/Agent-Level-Up (3 stars, last pushed 17d ago), licensed MIT. It adds 85 tokens to every session and 2,792 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to assessing-vector-and-embedding-weaknesses, differing in 9 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.
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Use when the user wants to import bulk data into a Milvus collection via Zilliz Cloud import jobs, or manage import stages (pre-uploaded file holders that import jobs reference).
vector
Use when the user wants to search, query, insert, upsert, get, or delete vectors in a Milvus collection.
rag-perf
Performance benchmarking for a deployed NVIDIA RAG Blueprint server: profiling pass + aiperf load test driven by a single YAML config. Not for accuracy / RAGAS scoring (use rag-eval) or for deploying / repairing services (use rag-blueprint).