Skip to main content

Context Window Overflow: Spamming the RAG Pipeline

The Limited Memory of AI​

Every LLM has a limit: the Context Window. Whether it's 8k, 32k, or 128k tokens, it is finite. In a RAG (Retrieval-Augmented Generation) system, this window is shared between:

  1. The System Prompt (Security rules).
  2. The User Query.
  3. Retrieved Documents (The Data).

Context Window Overflow is an attack where a malicious actor floods the retrieval system with "garbage" or repetitive data to push legitimate instructions out of the model's "memory."

How the Attack Works​

  1. The Injection: An attacker uploads a document (e.g., a ticket in Jira or a PDF) containing thousands of repeated keywords or nonsensical text related to a target topic.
  2. The Retrieval: When a user asks a question, the vector database finds this "keyword-stuffed" document because it mathematically looks highly relevant.
  3. The Overflow: The RAG system inserts this massive chunk into the prompt.
  4. The Failure: The LLM truncates the input to fit the window. Often, the System Prompt (which usually sits at the start or end) gets cut off.

Without the System Prompt, the LLM loses its identity, safety guardrails, and business logic. It becomes a raw, unaligned model prone to hallucinations and manipulation.

It's Not Just an Attack, It's a Cost Issue​

Even if the attack doesn't hijack the model, it burns tokens. Processing 100k tokens of garbage for every query can spike API costs by 100x overnight. This is a form of Economic Denial of Service (DoS).

Preventing Overflow with Data Hygiene​

The solution lies in Data Governance. You must ensure that files entering your knowledge base are legitimate and information-dense.

Veritensor helps prevent this by scanning datasets and documents for anomalies before ingestion:

  • Repetition Detection: Identifying files with abnormal keyword stuffing.
  • Token Limits: Flagging documents that are suspiciously large or dense compared to the norm.
  • Malicious URLs: Detecting spam links often associated with garbage content.

Clean data means a secure and cost-effective RAG pipeline.