# Tracing Target Answers in Poisoned Retrieval Corpora via Token Influence Attribution

arXiv: https://arxiv.org/abs/2606.25721 · HTML: https://mlsanity.com/paper/2606.25721/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 32 of its 40 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 30 of 32)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [Unsupervised Dense Information Retrieval with Contrastive Learning](https://mlsanity.com/paper/2112.09118/) (2021)
- [Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs](https://mlsanity.com/paper/2503.01743/) (2025)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [Poisoning Retrieval Corpora by Injecting Adversarial Passages](https://mlsanity.com/paper/2310.19156/) (2023)
- [Certifiably Robust RAG against Retrieval Corruption](https://mlsanity.com/paper/2405.15556/) (2024)
- [Machine Against the RAG: Jamming Retrieval-Augmented Generation with Blocker Documents](https://mlsanity.com/paper/2406.05870/) (2024)
- [InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales](https://mlsanity.com/paper/2406.13629/) (2024)
- [Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations](https://mlsanity.com/paper/2404.13948/) (2024)
- [Pandora: Jailbreak GPTs by Retrieval Augmented Generation Poisoning](https://mlsanity.com/paper/2402.08416/) (2024)
- ["Glue pizza and eat rocks" -- Exploiting Vulnerabilities in Retrieval-Augmented Generative Models](https://mlsanity.com/paper/2406.19417/) (2024)
- [Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation](https://mlsanity.com/paper/2405.20485/) (2024)
- [TrustRAG: Enhancing Robustness and Trustworthiness in Retrieval-Augmented Generation](https://mlsanity.com/paper/2501.00879/) (2025)
- [RevPRAG: Revealing Poisoning Attacks in Retrieval-Augmented Generation through LLM Activation Analysis](https://mlsanity.com/paper/2411.18948/) (2024)
- [Topic-FlipRAG: Topic-Orientated Adversarial Opinion Manipulation Attacks to Retrieval-Augmented Generation Models](https://mlsanity.com/paper/2502.01386/) (2025)
- [SafeRAG: Benchmarking Security in Retrieval-Augmented Generation of Large Language Model](https://mlsanity.com/paper/2501.18636/) (2025)
- [One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2505.11548/) (2025)
- [FlippedRAG: Black-Box Opinion Manipulation Adversarial Attacks to Retrieval-Augmented Generation Models](https://mlsanity.com/paper/2501.02968/) (2025)
- [Corpus Poisoning via Approximate Greedy Gradient Descent](https://mlsanity.com/paper/2406.05087/) (2024)
- [Secure Retrieval-Augmented Generation against Poisoning Attacks](https://mlsanity.com/paper/2510.25025/) (2025)
- [ReliabilityRAG: Effective and Provably Robust Defense for RAG-based Web-Search](https://mlsanity.com/paper/2509.23519/) (2025)
- [Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks](https://mlsanity.com/paper/2412.16708/) (2024)
- [SeCon-RAG: A Two-Stage Semantic Filtering and Conflict-Free Framework for Trustworthy RAG](https://mlsanity.com/paper/2510.09710/) (2025)
- [GRADA: Graph-based Reranking against Adversarial Documents Attack](https://mlsanity.com/paper/2505.07546/) (2025)
- [ADMIT: Few-shot Knowledge Poisoning Attacks on RAG-based Fact Checking](https://mlsanity.com/paper/2510.13842/) (2025)
- [CtrlRAG: Black-box Document Poisoning Attacks for Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2503.06950/) (2025)
- [RAGuard: A Novel Approach for in-context Safe Retrieval Augmented Generation for LLMs](https://mlsanity.com/paper/2509.03768/) (2025)
- [RAG-Pull: Turning Retrieval into a Code-Injection Channel via Invisible Unicode Perturbations](https://mlsanity.com/paper/2510.11195/) (2025)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) systems are vulnerable to corpus poisoning attacks that manipulate model outputs through malicious retrieved documents. Existing detection methods typically rely on auxiliary classifiers or additional LLM-based verification, introducing substantial computational overhead. We present TRACE, a lightweight detection framework that identifies poisoning attacks by tracing answer-related tokens through token influence attribution. TRACE first discovers recurrent high-influence keywords across retrieved documents and then performs a secondary verification to confirm their influence on model predictions. Experiments on three QA benchmarks and six LLMs demonstrate strong detection performance while simultaneously uncovering attacker-specified target answers.