TLDR: SeCon-RAG is a new framework that makes Retrieval-Augmented Generation (RAG) systems more trustworthy by defending against data poisoning and contamination attacks. It uses a two-stage process: first, Semantic and Cluster-Based Filtering (SCF) to remove malicious documents, and then Conflict-Aware Filtering (CAF) to ensure semantic consistency before generating answers. This approach preserves valuable information while significantly improving the robustness and reliability of RAG outputs.
Retrieval-Augmented Generation (RAG) systems have become crucial for enhancing large language models (LLMs) by providing them with external knowledge. This helps LLMs overcome limitations like outdated information and hallucinations. However, RAG systems face a significant challenge: they are vulnerable to attacks where malicious content is injected into their knowledge base, known as corpus poisoning or contamination attacks. These attacks can compromise the integrity and trustworthiness of the LLM’s output.
Existing defense mechanisms often employ aggressive filtering, which, while effective at removing harmful content, can inadvertently discard valuable and legitimate information. This leads to a reduction in the overall reliability and quality of the generated responses. To address this critical problem, researchers have developed a novel framework called SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG. This framework aims to provide robust defense without sacrificing useful knowledge.
Understanding SeCon-RAG’s Core Components
At the heart of SeCon-RAG is the Entity-Intent-Relation Extractor (EIRE). EIRE is a sophisticated semantic extraction module designed to capture the high-level meaning of any document. It breaks down text into three fundamental structural components: key entities (specific terms, names, data, locations), the underlying intent or objective conveyed by the passage, and the semantic relationships between the extracted entities. By providing this fine-grained semantic understanding, EIRE lays the groundwork for SeCon-RAG’s filtering stages.
Stage One: Semantic and Cluster-Based Filtering (SCF)
The first line of defense in SeCon-RAG is the Semantic and Cluster-Based Filtering (SCF) module, which operates before documents are retrieved for generation. This stage employs a dual filtering mechanism to identify and eliminate poisoned or adversarial content:
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Clustering-Based Filtering: Adversarial documents often share similar phrasing or templated structures, causing them to form tight clusters in an embedding space. SCF uses K-means clustering to identify these suspicious clusters and filters out documents that are too tightly grouped around a centroid, as these are likely to be malicious.
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Semantic Graph-Based Filtering: Relying solely on vector similarity can sometimes lead to false negatives. To overcome this, SCF uses EIRE to construct semantic relevance graphs for documents. Correct documents tend to produce densely connected and coherent semantic graphs, while poisoned documents often exhibit sparse, fragmented, or unnatural connections. By comparing a candidate document’s semantic graph to a benchmark set of verified correct documents, SCF assesses its semantic similarity and filters out those that deviate significantly.
To ensure robustness and minimize the loss of valuable information, SCF uses a conservative AND-based filtering strategy. Only documents flagged by both the clustering and semantic filters are removed, creating a refined and cleaner corpus for retrieval.
Stage Two: Conflict-Aware Filtering (CAF)
Even after the initial SCF, some documents might remain that are not overtly malicious but could be semantically irrelevant or internally inconsistent. These residual conflicts can still undermine the factual reliability of the final answer. This is where the Conflict-Aware Filtering (CAF) module comes into play during the inference stage.
For each retrieved document, CAF uses EIRE to extract semantic information (entities, intent, relations) and then evaluates its consistency across three dimensions:
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Query Consistency: Does the document semantically align with the user’s original query?
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Corpus Consistency: Is the document consistent with other retrieved documents?
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Model Consistency: Is the document factually compatible with the LLM’s own internal knowledge?
Based on these checks, the LLM determines whether the information is trustworthy, poisoned, conflicting, or irrelevant. Only trustable information is then used for generating the final response. This layered approach ensures that the generation module operates on a semantically coherent, query-relevant, and factually aligned knowledge base, significantly boosting robustness and factual faithfulness.
Also Read:
- RIPRAG: A Reinforcement Learning Approach to Black-Box Poisoning of RAG Systems
- Enhancing Web Safety: A Multi-Agent LLM Framework for Misinformation Defense
Experimental Validation and Performance
Extensive experiments were conducted across various LLMs (including LLaMA-3.1-8B, Mistral-12B, GPT-4o, DeepSeek-R1, and Qwen-7B) and datasets (Natural Questions, HotpotQA, MS-MARCO). SeCon-RAG consistently outperformed state-of-the-art defense methods in terms of both generation robustness and output trustworthiness. It demonstrated high accuracy and low attack success rates under various adversarial scenarios, including prompt injection attacks and different levels of corpus poisoning (20% and 100%). Importantly, SeCon-RAG also maintained strong performance on clean corpora, indicating that its defense mechanisms do not degrade benign operations.
Ablation studies confirmed the critical contributions of both the SCF and CAF modules, as well as the importance of EIRE and a small set of verified correct documents. While SeCon-RAG introduces a moderate runtime overhead due to its multi-stage semantic processing, this trade-off is considered acceptable for many real-world RAG applications that prioritize robustness and correctness. The framework also showed consistent performance across different embedding models and was not overly sensitive to its filtering thresholds, making it practical for deployment.
In conclusion, SeCon-RAG offers a scalable and interpretable defense for RAG systems operating in adversarial environments. By combining coarse-grained statistical pruning with fine-grained semantic validation, it significantly enhances the trustworthiness and accuracy of LLM-generated outputs. For more details, you can refer to the original research paper.
