The paper proposes a generic, standard model construction for Anamorphic Key Encapsulation Mechanisms (AKEM) that achieves strong IND-CCA security, addressing a major gap in covert communication crypt…

The paper introduces the Asymmetric Collaborative Framework (ACF), a novel method that enables robust covert communication between autonomous agents despite inherent cognitive asymmetry caused by dyna…

This paper develops provably undetectable and robust watermarking schemes for LLM outputs even when the per-token entropy is only constant, removing previous dependencies on high entropy rates or larg…

The paper proposes Information Sufficiency (IS) as a comprehensive framework for privacy-preserving LLM communication, demonstrating that free-text pseudonymization outperforms existing suppression an…

The paper constructs high-rate public-key pseudorandom codes (PRCs) robust against edit errors, providing the first such binary constructions under assumptions that yield Hamming-robust PRCs.

The paper proposes CAMP, a cross-turn privacy framework that mitigates Cumulative PII Exposure (CPE) in multi-turn LLM conversations by tracking and masking accumulated personal data across the entire…

The paper develops a novel attack method for multi-agent discussions under continuous monitoring, demonstrating that monitoring alone is insufficient to secure these systems.

The paper demonstrates that soft fusion in multi-warden covert communication has structural limits, showing that the Fusion Center gains no significant detection advantage from randomizing the number…

The paper proposes Sello, a novel protocol that allows an owner to reconstruct a tamper-evident and verifiable record of AI agent actions by having a trusted receiver sign and publish receipts of the…

This paper demonstrates a novel, multi-stage privacy-leakage attack chain against black-box chatbot agents by combining indirect prompt injection with web-tool invocation, showing that such attacks ar…

This paper demonstrates a novel attack against the shuffling defense used in secure Transformer inference, showing that randomly permuted activations can still be exploited to recover model weights.

The paper introduces AgentSecBench, a security evaluation framework that measures prompt injection, privacy leakage, and tool-use integrity in LLM agents by defining formal security games and testing…

This paper analyzes the failure of current embedding-based defenses in multi-agent LLM systems and proposes using token-level confidence scores (logits) for improved robustness.

The paper introduces $I$-$(OT)^2$, a novel base 1-out-of-2 Oblivious Transfer (OT) protocol designed to minimize computation and interaction for resource-constrained IoT devices.

The paper proposes using deep learning to empirically test the indistinguishability of various post-quantum and hybrid cryptographic schemes, finding that no tested combination showed a significant ad…

The paper proposes a provably secure steganography scheme based on list decoding that significantly increases embedding capacity for Large Language Models (LLMs) compared to existing methods.

The paper proposes an efficient and provably secure linguistic steganography method using range coding that achieves high embedding capacity and speed, outperforming existing methods.

The paper introduces PACZero, a novel PAC-private fine-tuning mechanism that achieves usable utility for large language models while providing strong resistance against membership-inference attacks.

The paper introduces CIPL, a unified channel-oriented framework, demonstrating that privacy leakage in LLM agents is governed by observable data channels and pipeline interactions, rather than being l…

The paper introduces PrivacyPeek, a new benchmark that audits the acquisition stage of LLM-based agents to demonstrate that unnecessary acquisition of sensitive data is a widespread and critical priva…