ASINT: Learning AS-To-Organization Mapping from Internet Metadata

Accurately mapping Autonomous Systems ASNs to their owning or operating organizations underpins Internet measurement research and security applications. Yet existing approaches commonly rely solely on WHOIS or PeeringDB, missing important relationships e.g., cross-regional aliases, parent-child...

ARPaCCino: an Agentic-RAG for Policy As Code Compliance

Policy as Code PaC is a paradigm that encodes security and compliance policies into machine-readable formats, enabling automated enforcement in Infrastructure as Code IaC environments. However, its adoption is hindered by the complexity of policy languages and the risk of misconfigurations. In th...

The Hidden Threat in Plain Text: Attacking RAG Data Loaders

Large Language Models LLMs have transformed human-machine interaction since ChatGPT's 2022 debut, with Retrieval-Augmented Generation RAG emerging as a key framework that enhances LLM outputs by integrating external knowledge. However, RAG's reliance on ingesting external documents introduces new...

Rethinking and Exploring String-Based Malware Family Classification in the Era of LLMs and RAG

Malware Family Classification MFC aims to identify the fine-grained family e.g., GuLoader or BitRAT to which a potential malware sample belongs, in contrast to malware detection or sample classification that predicts only an Yes/No. Accurate family identification can greatly facilitate automated...

SV-LLM: an Agentic Approach for SoC Security Verification Using Large Language Models

Ensuring the security of complex system-on-chips SoCs designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models LLMs, with their...

AURA: a Multi-Agent Intelligence Framework for Knowledge-Enhanced Cyber Threat Attribution

Effective attribution of Advanced Persistent Threats APTs increasingly hinges on the ability to correlate behavioral patterns and reason over complex, varied threat intelligence artifacts. We present AURA Attribution Using Retrieval-Augmented Agents, a multi-agent, knowledge-enhanced framework fo...

On Automating Security Policies with Contemporary LLMs

The complexity of modern computing environments and the growing sophistication of cyber threats necessitate a more robust, adaptive, and automated approach to security enforcement. In this paper, we present a framework leveraging large language models LLMs for automating attack mitigation policy...

Dot 跨站脚本漏洞

Dot is a text-to-speech, RAG and LLM tool by alexpinel individual developers. A cross-site scripting vulnerability exists in Dot 0.9.3 and earlier versions, which stems from user input and LLM output being appended to the DOM using innerHTML, which could lead to cross-site scripting and command...

Adversarial Threat Vectors and Risk Mitigation for Retrieval-Augmented Generation Systems

Retrieval-Augmented Generation RAG systems, which integrate Large Language Models LLMs with external knowledge sources, are vulnerable to a range of adversarial attack vectors. This paper examines the importance of RAG systems through recent industry adoption trends and identifies the prominent...

Spa-VLM: Stealthy Poisoning Attacks on RAG-Based VLM

With the rapid development of the Vision-Language Model VLM, significant progress has been made in Visual Question Answering VQA tasks. However, existing VLM often generate inaccurate answers due to a lack of up-to-date knowledge. To address this issue, recent research has introduced...

Benchmarking Poisoning Attacks against Retrieval-Augmented Generation

Retrieval-Augmented Generation RAG has proven effective in mitigating hallucinations in large language models by incorporating external knowledge during inference. However, this integration introduces new security vulnerabilities, particularly to poisoning attacks. Although prior work has explore...

Silent Leaks: Implicit Knowledge Extraction Attack on RAG Systems through Benign Queries

Retrieval-Augmented Generation RAG systems enhance large language models LLMs by incorporating external knowledge bases, but they are vulnerable to privacy risks from data extraction attacks. Existing extraction methods typically rely on malicious inputs such as prompt injection or jailbreaking,...

Scalable Defense against In-The-Wild Jailbreaking Attacks with Safety Context Retrieval

Large Language Models LLMs are known to be vulnerable to jailbreaking attacks, wherein adversaries exploit carefully engineered prompts to induce harmful or unethical responses. Such threats have raised critical concerns about the safety and reliability of LLMs in real-world deployment. While...

RAR: Setting Knowledge Tripwires for Retrieval Augmented Rejection

Content moderation for large language models LLMs remains a significant challenge, requiring flexible and adaptable solutions that can quickly respond to emerging threats. This paper introduces Retrieval Augmented Rejection RAR, a novel approach that leverages a retrieval-augmented generation RAG...

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems

Large Language Models LLMs enhanced with Retrieval-Augmented Generation RAG have shown improved performance in generating accurate responses. However, the dependence on external knowledge bases introduces potential security vulnerabilities, particularly when these knowledge bases are publicly...

TechniqueRAG: Retrieval Augmented Generation for Adversarial Technique Annotation in Cyber Threat Intelligence Text

Accurately identifying adversarial techniques in security texts is critical for effective cyber defense. However, existing methods face a fundamental trade-off: they either rely on generic models with limited domain precision or require resource-intensive pipelines that depend on large labeled...

ThreatLens: LLM-Guided Threat Modeling and Test Plan Generation for Hardware Security Verification

Current hardware security verification processes predominantly rely on manual threat modeling and test plan generation, which are labor-intensive, error-prone, and struggle to scale with increasing design complexity and evolving attack methodologies. To address these challenges, we propose...

POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models

Large language models LLMs have achieved remarkable success in various domains, primarily due to their strong capabilities in reasoning and generating human-like text. Despite their impressive performance, LLMs are susceptible to hallucinations, which can lead to incorrect or misleading outputs...

System Prompt Poisoning: Persistent Attacks on Large Language Models beyond User Injection

Large language models LLMs have gained widespread adoption across diverse applications due to their impressive generative capabilities. Their plug-and-play nature enables both developers and end users to interact with these models through simple prompts. However, as LLMs become more integrated in...

Defending against Indirect Prompt Injection by Instruction Detection

The integration of Large Language Models LLMs with external sources is becoming increasingly common, with Retrieval-Augmented Generation RAG being a prominent example. However, this integration introduces vulnerabilities of Indirect Prompt Injection IPI attacks, where hidden instructions embedded...