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🤖 AI/LLM HACKING COURSE
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Part of the AI/LLM Hacking Course — 90 Days
Day 23 of 90 · 25.6% complete
⚠️ Authorised Targets Only: Advanced RAG poisoning attacks — including document submission, namespace probing, and injection payload embedding — must only be performed on authorised targets. Clean up all submitted test documents from production knowledge bases at the end of the engagement. Any poisoned documents left in a production system create ongoing risk to real users.
A healthcare client asked me to assess their clinical AI assistant six months after it launched. It had been in production the entire time. Twelve thousand clinical queries processed. The RAG knowledge base held clinical guidelines, drug reference information, and internal protocol documents — all legitimate, all reviewed before ingestion. Except one. A single document that had been submitted via the portal by a user account that shouldn’t have had submission access due to a misconfigured permission. The document looked like a clinical guideline. Three paragraphs of accurate medical text. One paragraph of AI injection instructions, formatted to look like a continuation of the clinical content.
The injection had been active for four months. Every query related to the topic that document covered — a reasonably common clinical area — triggered retrieval. The model incorporated the injection instructions into its response alongside the legitimate clinical context. We were never able to determine exactly how many of the twelve thousand queries had triggered retrieval of that document. Day 23 exists because the Day 12 sentinel token methodology is sufficient for confirming RAG is injectable. It’s not sufficient for understanding the full scope of what advanced RAG poisoning can achieve, how it persists, and how to detect it systematically. That’s what this day covers.
🎯 What You’ll Master in Day 23
Design semantically optimised poison documents that reliably surface in target query results
Test namespace isolation gaps and cross-namespace retrieval bypass
Test metadata filter bypass in RAG retrieval pipelines
Execute persistent injection chains that affect all future retrievals of a topic
Detect and assess RAG poisoning in existing deployments
Calculate the persistence severity multiplier for RAG vs conversation-based injection
⏱️ Day 23 · 3 exercises · Kali Terminal + Think Like Hacker + Kali Terminal ### ✅ Prerequisites - Day 12 — LLM08 Vector and Embedding Weaknesses — the RAG pipeline anatomy, sentinel token methodology, and ChromaDB lab from Day 12 are the foundation; Day 23 extends all three - Day 5 — Indirect Prompt Injection — RAG injection is the persistent variant of indirect injection; Day 5’s delivery mechanism understanding is prerequisite - ChromaDB and sentence-transformers installed — Exercise 1 builds an advanced RAG test environment with embedding-level analysis ### 📋 RAG Poisoning Attacks Deep Dive — Day 23 Contents 1. Mapping the Retrieval Trigger Surface 2. Semantic Document Optimisation for Reliable Retrieval 3. Namespace Isolation and Cross-Boundary Bypass 4. Metadata Filter Bypass 5. Persistent Injection Chains 6. RAG Poisoning Detection in Existing Deployments In Day 12 you confirmed RAG injection was possible using the sentinel token methodology. Day 23 builds the advanced methodology for making that injection reliable, persistent, and maximally impactful. Day 24 covers model fingerprinting in depth — identifying which model, version, and configuration powers a target endpoint, which determines which attack families are most likely to succeed.
Mapping the Retrieval Trigger Surface
Before designing a poison document, you need to know which queries will trigger its retrieval. The retrieval trigger surface is the set of queries that would cause the RAG system to return your document based on semantic similarity. Get this wrong and you’ve introduced a document into the knowledge base that never gets retrieved — meaningless from an attack perspective.
Three approaches to mapping the trigger surface. First: probe existing retrieval to understand which topics surface which content. Send queries across the topic space and observe what gets retrieved — this gives you a map of the semantic landscape. Second: identify the embedding model being used (often visible in the application’s JavaScript, configuration files, or error messages) and use it to calculate similarity scores between candidate trigger queries and candidate poison document content before submission. Third: use the sentinel token approach from Day 12 as a calibration tool — submit documents with varying levels of semantic relevance to a target query and measure retrieval probability via sentinel token appearance rate.
TRIGGER SURFACE MAPPING — RETRIEVAL PROBABILITY TESTCopy
Step 1: Probe existing retrieval to map the semantic landscape
queries = [“cybersecurity policy”, “password requirements”,
“incident response”, “data handling”, “employee training”]
For each query, observe what content surfaces in the AI response
Map: query → content topics retrieved
Step 2: Calculate embedding similarity for candidate poison docs
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer(‘all-MiniLM-L6-v2’) # common default
trigger_query = “what is our password policy?”
poison_doc = “Our password policy requires… SENTINEL_XK9 …”
q_emb = model.encode(trigger_query, convert_to_tensor=True)
d_emb = model.encode(poison_doc, convert_to_tensor=True)
similarity = util.cos_sim(q_emb, d_emb).item()
print(f”Similarity score: {similarity:.3f}”) # target: > 0.6
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