Roberto Di Pietro

Elia Onofri, Andrea De Salve, Paolo Mori et al.

The Self-Sovereign Identity (SSI) paradigm is instrumental for decentralised identity management, allowing an entity to create, manage, and present their digital credentials without relying on centralised authorities. Credential selective disclosure is one of the most attractive privacy-preserving features of SSI, allowing users to reveal only the minimum necessary information from their credentials. However, current selective disclosure mechanisms primarily focus on protecting the privacy of credential Holders, while offering limited protection to the Verifiers of credentials. Indeed, the specific credential information requested by a Verifier can inadvertently reveal to credential Holders sensitive information, including internal decision-making criteria, business rules, or strategic plans. In this work, we address this threat by proposing, to the best of our knowledge, the first approach that enforces mutual privacy in credential exchanges. To this end, we introduce COD-ssi (Claim Oblivious Disclosure for SSI), a novel framework that leverages Oblivious Pseudorandom Functions to allow Verifiers to selectively access a subset of claims without revealing which specific claims were accessed to the credential Holder. The security of our solution is formally verified and its feasibility is assessed through the experimental evaluation of our open-source prototype implementation. These results show that provable mutual privacy in the context of SSI can be achieved with just moderate computational and communication overhead.

Elia Onofri, Andrea Ciccotelli, Roberto Di Pietro

Oblivious Transfer (OT) is a fundamental cryptographic primitive enabling privacy-preserving computation and constitutes a core building block for secure multi-party computation while supporting a wide range of security-sensitive applications: private information retrieval, zero-knowledge proofs, and password-authenticated key exchange, to cite a few. While recent advances in OT extension have significantly reduced amortised costs, their reliance on batches of random base OTs and substantial pre-computation phases limits their practicality in scenarios where the number of transfers is modest or where communication latency and client-side computation are critical constraints. In such settings, efficient base OT protocols remain both relevant and necessary. In this work, we introduce $I$-$(OT)^2$, a novel base 1-out-of-2 OT protocol grounded in the quadratic residuosity problem, specifically designed to minimise receiver-side computation and interaction. Our construction is particularly appealing on client--server architectures in which the receiver operates on low-power hardware, such as Internet of Things (IoT) devices. Through a lightweight offline pre-computation phase, $I$-$(OT)^2$ shifts the on-transfer computational burden almost entirely to the Sender, while reducing online communication to only six messages and four digests exchanged. We provide a detailed description of the protocol, accompanied by a formal proof of its security. Moreover, to demonstrate the viability of $I$-$(OT)^2$, we also present an open-source proof-of-concept implementation (in C language) evaluated on real IoT hardware. Results are staggering: for 128-bit security using a 3072-bit RSA modulus, the receiver incurs an average online cost per OT as low as 2.80 μs on desktop platforms and 39.90 μs on IoT devices, more than 10$\times$ faster than the well known SimplestOT.

Ahmed Mahrous, Roberto Di Pietro

Cryptocurrency pump-and-dump schemes coordinated via Telegram threaten market integrity. However, existing research addressing this specific threat has not yet produced solutions that combine reliable results with fast response. This is in part due to the absence of publicly available, message-level labeled data, as well as design choices. In this paper, we address both issues. In particular, we introduce a corpus of over 280,000 Telegram posts from 39 pump-organizing groups, all manually reviewed to identify 2,246 pump announcements and their targeted cryptocurrency and exchange. Leveraging this dataset, we define two tasks: real-time pump-announcement detection and target cryptocurrency/exchange extraction. For detection, we compare two machine-learning models: a lightweight tree-based LightGBM classifier (F1=0.79, latency=9.4 s/sample) and a transformer-based BGE-M3 (F1=0.83, latency=50 ms/sample). With our proposed approach, we show that message analysis can achieve near-instant pump detection at the level of individual Telegram message windows. Unlike prior work that relies purely on market data and typically detects pumps tens of seconds after abnormal trading activity is observed, our method operates directly on the coordination messages themselves and can be evaluated in microseconds per window on commodity hardware. To our knowledge, we also establish the first benchmark for manipulated coin and exchange extraction. We demonstrate that traditional rule-based extraction methods, widely relied upon in prior literature, are ineffective due to ticker ambiguity. In contrast, LLMs achieve the highest accuracy with a score of 0.91.

Yimeng Chen, Nathanaël Denis, Roberto Di Pietro et al.

Self-hosted AI agents read and write their own memory and configuration files to function. An agent may get compromised via corruption of its own state -- a compromise realized via legitimate OS system call invocation. We refer to this class of threats as self-state attacks. In this paper, we investigate the OS resilience to this class of attacks. Formally, we characterize a four-axis attack space (Target, Mechanism, Granularity, Temporal); investigate the structural limits of prevention, detection, and recovery; and introduce a workload-conditioned view of detectability. To instantiate the framework, we collect live activity traces from a representative self-hosted agent running across distinct workload profiles, and realize the attack space as a 23-cell matrix, 43 concrete operations on real self-state files, and injected into those traces. We then evaluate both canonical and workload-conditioned defense strategies. The empirical results show that a layered defense stack (access-control prevention on the instruction and configuration layers, workload-conditioned detection on the memory layer, and periodic backup for recovery) is effective on most attack cells while a small residual attack surface remains structurally indistinguishable at the OS level. These findings suggest that against the newly established class of self-state attacks, OS-level defense needs to be reconsidered, potentially opening new research directions in the field.