The paper investigates how large language models (LLMs) misinterpret office documents due to differences between the text extracted from Office Open XML (OOXML) files and what humans actually see. By analyzing common extractors, the authors identify 19 hidden OOXML channels and 12 content omissions, and demonstrate that these gaps enable five attacks that mislead major chat and coding assistants. They propose Prism, a lightweight defense that reconstructs the human view from OOXML before feeding text to the model, which generalizes to unseen attack payloads.
arXiv:2510. 01359v2 Announce Type: replace-cross Abstract: Code-capable large language model (LLM) agents are embedded in software engineering workflows where they can read, write, and execute code, raising "jailbreak" stakes beyond text-only settings.
The paper introduces a three‑layer security framework designed to protect retrieval‑augmented generation (RAG) chatbots from both direct and indirect prompt injection attacks. Layer 1 filters user input with rule‑based patterns and a semantic anomaly classifier; Layer 2 enforces a provenance‑based instruction hierarchy during context assembly; Layer 3 audits model output with a policy rule engine and semantic drift detector. Evaluations on GPT‑4o, Llama 3, and Mistral 7B demonstrate a reduction in attack success rate from 71.4 % to 11.3 %, outperforming existing single‑layer defenses while keeping false positives low and latency acceptable.
arXiv:2509. 20324v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) is an emerging approach in natural language processing that combines large language models (LLMs) with external document retrieval to produce more accurate and grounded responses.
arXiv:2606. 31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators.
The paper introduces Semantic Overlays, a steering technique that adds non‑textual annotations to a language model’s input by applying learned adapters at specific prefill positions. These overlays create an out‑of‑band channel that encodes span identity and complex semantics, enabling the model to interpret marked text differently—such as rewriting code in a specified language or ignoring executable instructions. Experiments show that Semantic Overlays dramatically reduce prompt‑injection success rates while preserving model utility and readability of marked spans.
arXiv:2610. 01871v1 Announce Type: cross Abstract: Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a reliable and cost-effective technique of grounding the generative capabilities of Multimodal Large Language Models (MLLMs) into relevant, up-to-date, external knowledge.
arXiv:2603.18740v3 Announce Type: replace-cross Abstract: Automated Code Review (ACR) systems integrating Large Language Models (LLMs) are increasingly adopted in software development workflows, rang...
arXiv:2606. 02240v1 Announce Type: cross Abstract: Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, Salesforce, or Jira accessed through tool calls) whose response content the user neither writes nor controls.
arXiv:2609.14003v1 Announce Type: cross Abstract: Personal AI agents built on large language models (LLMs) are increasingly given access to a user's private data and communications in order to provid...
arXiv:2607. 26339v1 Announce Type: new Abstract: 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.
arXiv:2505. 14300v2 Announce Type: replace Abstract: White-box monitoring is increasingly adopted as an auditing tool as Large Language Models (LLMs) are deployed in daily operations to ensure safe model behavior.
The paper investigates how large language model (LLM)–based automated code review (ACR) systems can be manipulated through contextual bias injection, a framing effect that lets presentation override content. A large‑scale study across six LLMs confirms the framing effect in vulnerability detection, and a realistic experiment on 33 CVEs in 20 projects shows that a novel LLM‑assisted refinement attack succeeds in 97% of cases, while a template‑based attack fails. The results underscore the vulnerability of real‑world ACR pipelines and the need for human oversight and trust in the development process.