This paper provides a large-scale empirical analysis of indirect prompt injections found in webpages, revealing that prompt-based interference is a widespread, persistent, and growing threat targeting…

This paper demonstrates that visual phishing detectors can be completely bypassed by employing simple timing-based attacks that delay the rendering of key webpage elements.

HunterAgent is a neuro-symbolic framework that reconstructs causal attack chains from fragmented, anti-forensics-corrupted logs, achieving high accuracy while drastically reducing hallucination.

PIIGuard introduces a novel webpage-level defense mechanism using optimized hidden HTML fragments to prevent LLM assistants from scraping contact-style PII, achieving high defense success rates while…

The paper proposes a secure-by-design Generative AI framework that integrates PromptShield for LLM security and CIAF for structured cloud forensic investigation, significantly improving both robustnes…

The paper introduces RAGCharacter, a forensic framework that enables black-box, character-level traceback to pinpoint the exact poisoned span in retrieved evidence responsible for a misgeneration even…

This paper introduces seven novel, cross-domain techniques for detecting prompt injection attacks, moving beyond the limitations of traditional regex and transformer classifiers.

The paper proposes extbackslash codeName, a behavioral firewall that uses a parameterized deterministic finite automaton (pDFA) to enforce verified benign tool-call sequences and parameter bounds for…

The paper proposes a lightweight hybrid MLP framework that uses structural URL features to achieve highly accurate and computationally efficient real-time phishing URL detection, outperforming several…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these honeypots provide substantially longer and harder-to-detect…

The paper introduces Honeyval, a comprehensive evaluation framework, to rigorously test LLM-powered HTTP honeypots, demonstrating that these systems provide substantially longer and harder-to-detect i…

The paper addresses the lack of user understanding regarding the actions and residual effects of advanced computer-use agents by proposing AgentTrace, a traceability framework for visualizing agent be…

The paper introduces IPI-proxy, an open-source intercepting proxy toolkit designed to red-team web-browsing AI agents by injecting adversarial payloads into live HTTP responses from whitelisted domain…

This paper systematically analyzes the threat posed by malicious third-party API routers in the LLM supply chain, finding that a significant number of routers actively perform payload injection, crede…

The paper introduces 'Routing Hijacking,' a severe attack where malicious clients forge semantic profiles in Federated RAG systems to misroute target queries, and proposes a trust-aware post-routing f…

The paper introduces RealVuln, a benchmark that demonstrates a clear three-tier performance hierarchy for security scanners on real-world code, with specialized tools significantly outperforming gener…

This study provides the first systematic measurement of prompt injection attacks in a real-world LLM-based resume screening application, finding that approximately 1% of resumes contain hidden injecti…

This study provides the first large-scale measurement of prompt injection attacks in real-world LLM-based resume screening, finding that approximately 1% of resumes contain hidden injections.

The paper proposes a lightweight, passive bot detection system using user-agent and favicon analysis on web server logs, achieving 67.7% bot detection with a low 3% false-positive rate.

CyberCane is a neuro-symbolic framework that enhances phishing detection by combining symbolic rule analysis with privacy-preserving RAG and formal ontology reasoning, achieving high recall against AI…