2026· Poster Volume 0008 The 2026 Twenty-Second International Conference on Intelligent Computing July 23-26, 2026 Toronto, Canada· 0 citations
TL;DR
Att2RAG is presented, a double-condition framework for knowledge poisoning attacks on RAG systems that decomposes a successful poisoning event into a retrieval condition and a generation condition, and casts poisoning as maximizing attack success subject to satisfying both conditions.
Abstract
Modern retrieval-augmented generation (RAG) and memoryaugmented LLM applications are widely deployed in knowledge-intensive
settings. These systems ground model outputs on external knowledge
stores and may persist interaction traces in vector memory. If the underlying store is compromised, poisoned content can be retrieved repeatedly
and thereby shape downstream responses, yielding confident yet harmful
outputs supported by seemingly plausible evidence. Recent studies have
shown that RAG pipelines and LLM agents are vulnerable to knowledge
poisoning and prompt injection, but many formulations treat attack
success as a single end-to-end outcome and do not separate retrieval
and generation failure modes in a retrieval–generation aligned manner.
Moreover, although long-horizon memory writes can introduce cumulative
risks, these effects are not empirically evaluated under our benchmark setting. We present Att2RAG, a double-condition framework for knowledge
poisoning attacks on RAG systems. Att2RAG decomposes a successful
poisoning event into a retrieval condition and a generation condition, and
casts poisoning as maximizing attack success subject to satisfying both
conditions. We instantiate the framework with (i) a white-box variant
that applies projected gradient descent (PGD) in embedding space, serving as an approximate upper bound under strong attacker assumptions,
and (ii) a black-box attack based on a Q ⊕ I construction that combines
problem self-similarity with adversarial instruction injection using only
query access. We evaluate Att2RAG on representative QA benchmarks
under several common RAG configurations and consider practical defenses
such as paraphrasing and duplicate filtering. Attack success is measured
by ASR and Target-F1 relative to an attacker-specified target response.
Across the evaluated configurations, Att2RAG attains attack success
rates in the mid-90% range without defenses; paraphrasing plus duplicate
filtering reduce ASR only modestly, and many attacks remain successful.
These results highlight limitations of semantic-similarity-driven retrieval
and suggest that strengthening RAG systems requires defenses beyond
surface-form rewriting and naive duplicate removal.
Retrieval-Augmented Generation (RAG) systems ground large language models (LLMs) in external corpora, but this reliance exposes them to corpus poisoning: maliciously injected passages that manipulate retrieved evidence. We introduce RAGuard, a layered defense against \emph{factual} corpus-poisoning attacks on RAG pipelines. The first layer adversarially fine-tunes a dense retriever on synthetic poisoned documents (fabricated facts, contradictions, and reasoning traps), teaching it to downrank malicious passages before generation. The second layer, the Zero-Knowledge Inference Patch ZKIP, is a label-free, black-box filter: for each retrieved document, it performs a leave-one-out decode and scores the document by the semantic shift and output-entropy change that its removal induces. ZKIP requires no poison labels, no ground-truth answers, and no access to model internals; it compares the model's own answers under counterfactual contexts. On poisoned Natural Questions at 5--30\% poison ratios, adversarial retriever training alone reduces but does not eliminate attack success, while ZKIP drives the measured attack success rate to 0.000 in every defended configuration, keeping Recall@5 within 0.03 of the clean-corpus baseline. Supervised analyses on both Natural Questions and BEIR (NFCorpus) confirm that the counterfactual signals ZKIP relies on carry learnable poison structure. The defense costs $k{+}1$ generator passes per query ($6\times$ for $k{=}5$); we analyze batching and early-stopping approximations that reduce this overhead. We also show that keyword-preserving poisons leave lexical retrievers such as BM25 essentially unaffected, an observation that delineates the boundary of the threat model. Code, datasets, and evaluation harnesses are released for reproducibility.
Memory is becoming a default subsystem in deployed LLM agents to provide persistent personalization and continuity. This naturally prompts a question: will memory system introduce new vulnerabilities into agents? Thus we propose InjecMEM, a novel memory injection attack paradigm that requires only a single interaction (no read/edit access to memory store) to steer later responses of related queries toward a pre-specified output. Guided by the retrieval-then-generate mechanism of memory systems, we craft the injection with a retriever-agnostic anchor and an adversarial command. The anchor contains high-recall topical cues so that downstream retrieval consistently associates the record with the target topic. The command is a short sequence optimized to remain effective under uncertain fused contexts, variable placements, and long prompts so that it reliably steers outputs once retrieved. We learn the command via gradient-based coordinate search, averaging over synthetic prompt templates and insertion positions, and extend it to joint optimization across backbones to study transfer. Evaluated across multiple memory systems and backbone models, InjecMEM achieves reliable topic-conditioned retrieval and targeted generation, remains effective under memory drift, and leaves non-target queries unaffected. Our results underscore the need to harden memory systems and provide a reproducible framework for studying agent memory.
Hanling Tian, Gengyu Zhang, Zeyang Sha et al.· 0 citations
KFS-RAG is proposed, a defense that mitigates information leakage by reformulating the retrieved context by identifying a small set of influential keywords from the retrieved context via an attention rollout plus a causal perturbation mechanism.
Ziliang Zhang, Yubo Zhu, Wei Tong et al.· 0 citations
Retrieval-augmented generation (RAG) improves the factuality of large language models by grounding responses in external documents, but it also exposes a critical security vulnerability: adversarial documents injected into the knowledge database can enter the context window and steer the model toward targeted incorrect answers. Existing post-retrieval defenses rely on instruction following, parametric knowledge, or text-level consistency, all of which can be imitated or optimized against by adaptive attackers. We propose RAGSentinel, a training-free, label-free defense for black-box RAG systems. RAGSentinel uses a surrogate encoder to measure query-conditioned hidden-state shifts induced by retrieved documents, removes shared topic directions, and filters poisoned documents as geometric outliers from a robust majority consensus. We prove that, under an honest-majority assumption and a representation-level separation condition, RAGSentinel exactly recovers a poison-free majority-sized context. Experiments across three question-answering datasets, three LLM families, and multiple poisoning attacks show that RAGSentinel consistently achieves low attack success rates while preserving competitive accuracy and remaining effective against adaptive attacks with full pipeline knowledge.
Yueyang Quan, Anjun Gao, Yu Xia et al.· 0 citations
Memory-augmented LLM agents rely on rich context for long-horizon reasoning and acting, yet their memory modules expose a persistent attack surface for malicious records, making the study of memory poisoning threats imperative. However, existing query-only attacks often fail to remain effective in two realistic and prevalent settings: large-scale benign memory pools and active input auditing. Consequently, current approaches fall short when facing the dual challenges of high retrieval competitiveness and rigorous semantic checks. To overcome these limitations, we propose MAFIA, a query-only Memory Attack framework via probing and Factual Injection against Audit, tailored to this extended threat model. Specifically, MAFIA introduces: (1) a placement strategy that ensures retrieval-competitive injection via memory probing, budget allocation, and scheduling; and (2) a payload design that bypasses audits using compact factual cloaks, preserving malicious effects while maintaining high semantic similarity. Extensive evaluations reveal that MAFIA achieves up to a 90.7% attack success rate while suppressing audit detection from a peak of 83.3% to at most 7.4%, exposing critical vulnerabilities across agentic memory systems. Code will be made publicly available at https://github.com/JiamingChen1234/MAFIA.
Jiamin Chen, Yisen Gao, Yanping Li et al.· 0 citations
Persistent memory makes false information durable: once a false statement is stored, it can be retrieved into future sessions that match it. We measure the cost of this failure mode using plainly worded false assertions generated in a single pass, with no instruction, trigger, or retriever optimization. Poisoning 1.2% of a LongMemEval corpus reduces accuracy from 0.850 to 0.300. A four-stage write-time screening pipeline that reaches 0.832 recall on indirect prompt injection while flagging 1.5% of trigger-word-laden benign text rejects 0 of 360 poisoned memories. We argue this exposes a boundary of content-only screening: distinguishing a false assertion from a true one generally requires external grounding beyond the text itself. We then evaluate provenance-weighted retrieval. The shipped weight is statistically indistinguishable from no defense (p=0.80), while a stronger weight recovers utility only by excluding untrusted content. In a mixed-provenance corpus where untrusted content is mostly benign, accuracy rises from 0.3167 to 0.7000; when the answer-bearing evidence itself arrives untrusted, evidence recall falls to zero and accuracy to 0.0417. Under the measured similarity regime, the additive provenance term has no usable setting: a weight strong enough to resist query-shaped poison is also strong enough to suppress legitimate untrusted evidence. We therefore argue for bounded occupancy constraints at retrieval rather than additive provenance penalties, and release the harnesses, corpora, and aggregate run reports.