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.
By Shoumik Saha, Jifan Chen, Sam Mayers, Sanjay Krishna Gouda, Zijian Wang, Varun Kumar
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.
By Gulshan Saleem, Nisar Ahmed, Muhammad Imran Zaman, Ali Hassan, Umar Mujahid
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.
By Atousa Arzanipour, Rouzbeh Behnia, Reza Ebrahimi, Kaushik Dutta
arXiv:2606. 31639v1 Announce Type: cross Abstract: Large language models are no longer only text generators.
By Seyed Bagher Hashemi Natanzi, Bo Tang
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.
By Joshua Penman
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.
By Maria Carmen Jica, Ali Satvaty, Suzan Verberne, Fatih Turkmen