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 adriannoes/awesome-agentic-ai --skill assessing-vector-and-embedding-weaknessesgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/assessing-vector-and-embedding-weaknesses)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/assessing-vector-and-embedding-weaknesses"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/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/adriannoes/awesome-agentic-ai/assessing-vector-and-embedding-weaknesses"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/assessing-vector-and-embedding-weaknesses.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 77 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.02726 |
| Opus 5.5 | $0.00010 | $0.01090 |
| Sonnet 5.5 | $0.00005 | $0.00545 |
| Haiku 4.5 | $0.00003 | $0.00273 |
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 1mo 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- assessing-vector-and-embedding-weaknesses — 100% identical, 2 lines differ
- assessing-vector-and-embedding-weaknesses — 91% identical, 9 lines differ
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
4 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.
- 1mo ago First seen · 234 lines · 26 tokens per session scan A 6f6ec3f2c621
assessing-vector-and-embedding-weaknesses is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (72 stars, last pushed 2d ago), licensed MIT. It adds 26 tokens to every session and 2,726 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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 Workflow Planner
Designs a complete Retrieval-Augmented Generation (RAG) pipeline for a given use case, including chunking strategy, embedding model selection, and retrieval approach.
pinecone-quickstart
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided…
pinecone-n8n
Build n8n workflows using the Pinecone Assistant node or Pinecone Vector Store node. Use when building RAG pipelines, chat-with-docs workflows, configuring Pinecone nodes in n8n, troubleshooting Pinecone n8n nodes, or asking about best practices for Pinecone in n8n.
pinecone-full-text-search
Create, ingest into, and query a Pinecone full-text-search (FTS) document index using the graduated document-schema API (Python SDK 10.0.0, API version 2026-07). Use when the user or agent asks to build a text search index on Pinecone, add dense or sparse vector fields, ingest documents, construct scoreby clauses…