arXiv Machine Learning
VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents
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arXiv AI
Jun 3
Inference Cost Attacks for Retrieval-Augmented Large Language Models
arXiv:2606. 02643v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamically retrieves and synthesizes information from external knowledge sources.
arXiv AI
Sep 1
Watch your steps: Dormant Adversarial Behaviors that Activate upon LLM Finetuning
The paper introduces FAB, an attack that uses meta‑learning to embed dormant adversarial behaviors into large language models (LLMs). These behaviors remain inactive until the model is finetuned by downstream users, at which point the model can exhibit unwanted actions such as unsolicited advertising, jailbreakability, or over‑refusal. FAB is shown to be effective across multiple LLMs and resilient to various finetuning settings.
arXiv AI
2d ago
Memetic Trojans: Social Contagions as Carriers of Adversarial Payloads in Agent Networks
arXiv:2610.00430v1 Announce Type: cross Abstract: Autonomous large language model (LLM) agents increasingly interact in network environments where adversarial content can propagate between agents. Kn...
arXiv Machine Learning
Jun 26
Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
arXiv:2510. 00586v3 Announce Type: replace Abstract: Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned documents for each target phrase.
arXiv AI
Sep 1
Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors
arXiv:2604.27426v2 Announce Type: replace-cross Abstract: Local fine-tuning datasets routinely contain sensitive secrets such as API keys, personal identifiers, and financial records. Although "local...
arXiv AI
Jul 14
A Low-Latency Fraud Detection Layer for Detecting Adversarial Interaction Patterns in LLM-Powered Agents
arXiv:2605. 01143v2 Announce Type: replace Abstract: Large Language Model (LLM)-powered agents demonstrate strong capabilities in autonomous task execution, tool use, and multi-step reasoning.