Retrieval-Augmented Generation (RAG) is quickly becoming the default way
to deploy large language models in enterprise environments.
Most security discussions around RAG focus on prompt injection,
jailbreaks, or model alignment. However, there is a critical blind spot
that is increasingly exploitable in production systems: the retrieval layer.
Retrieval Is Trusted by Default
In a typical RAG pipeline, retrieved documents are treated as trusted
context once they enter the prompt window.
The model has no way to distinguish:
- authoritative internal documents
- outdated or misleading content
- adversarially injected material
If a document is retrieved, it influences the output.
How Retrieval Poisoning Works
Retrieval poisoning does not rely on obvious malicious payloads.
Instead, attackers introduce documents that:
- mimic internal tone and authority
- subtly reinforce misleading narratives
- align semantically with legitimate content
These documents are then retrieved alongside trusted ones and shape
the model’s response without triggering prompt filters or guardrails.
Why Existing Defenses Miss This
Most AI security controls operate too late in the pipeline.
- Prompt injection filters act after retrieval
- Model guardrails cannot assess document provenance
- Content moderation focuses on surface-level violations
If the context is poisoned, the output will be too.
*What Retrieval-Aware Security Requires
*
Securing RAG systems means controlling what reaches the model, not just
how the model behaves.
Effective controls should include:
- cryptographic document provenance
- semantic anomaly detection
- authority-weighted retrieval
- separation between retrieval control and generation
These measures prevent poisoned context from influencing the model,
rather than attempting to fix responses afterward.
Why This Matters Now
RAG systems are rapidly moving into regulated environments:
finance, healthcare, legal, and government use cases.
In these contexts, trust in AI outputs depends directly on the integrity
of retrieved data.
Ignoring the retrieval layer turns RAG into an unmonitored supply chain.
Further Reading
A technical preprint detailing a realistic threat model and evaluation
of retrieval poisoning defenses is available on Zenodo:
https://zenodo.org/records/18449664
An overview of the research framework is available at:
https://sentinelrag.com
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