Training Language Model Agents to Find Vulnerabilities with CTF-Dojo

Large language models LLMs have demonstrated exceptional capabilities when trained within executable runtime environments, notably excelling at software engineering tasks through verified feedback loops. Yet, scalable and generalizable execution-grounded environments remain scarce, limiting...

Fortifying the Agentic Web: a Unified Zero-Trust Architecture against Logic-Layer Threats

This paper presents a Unified Security Architecture that fortifies the Agentic Web through a Zero-Trust IAM framework. This architecture is built on a foundation of rich, verifiable agent identities using Decentralized Identifiers DIDs and Verifiable Credentials VCs, with discovery managed by a...

CVE-2025-54873

RISC Zero is a zero-knowledge verifiable general computing platform based on zk-STARKs and the RISC-V microarchitecture. RISC packages risc0-zkvm versions 2.0.0 through 2.1.0 and risc0-circuit-rv32im and risc0-circuit-rv32im-sys versions 2.0.0 through 2.0.4 contain vulnerabilities where signed...

DVFS: a Dynamic Verifiable Fuzzy Search Service for Encrypted Cloud Data

Cloud storage introduces critical privacy challenges for encrypted data retrieval, where fuzzy multi-keyword search enables approximate matching while preserving data confidentiality. Existing solutions face fundamental trade-offs between security and efficiency: linear-search mechanisms provide...

SmartphoneDemocracy: Privacy-Preserving E-Voting on Decentralized Infrastructure Using Novel European Identity

The digitization of democratic processes promises greater accessibility but presents challenges in terms of security, privacy, and verifiability. Existing electronic voting systems often rely on centralized architectures, creating single points of failure and forcing too much trust in authorities...

CodeGuard: a Generalized and Stealthy Backdoor Watermarking for Generative Code Models

Generative code models GCMs significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models require computational resources and time, necessitating effective digital copyright protection to prevent unauthorized lea...

Verifiable Unlearning on Edge

Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edg...

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

Quantum Enhanced Entropy Pool for Cryptographic Applications and Proofs

This paper investigates the integration of quantum randomness into Verifiable Random Functions VRFs using the Ed25519 elliptic curve to strengthen cryptographic security. By replacing traditional pseudorandom number generators with quantum entropy sources, we assess the impact on key security and...

Emission Impossible: Privacy-Preserving Carbon Emissions Claims

Information and Communication Technologies ICT have a significant climate impact, and data centres account for a large proportion of the carbon emissions from ICT. To achieve sustainability goals, it is important that all parties involved in ICT supply chains can track and share accurate carbon...

Identity and Access Management for the Computing Continuum

The computing continuum introduces new challenges for access control due to its dynamic, distributed, and heterogeneous nature. In this paper, we propose a Zero-Trust ZT access control solution that leverages decentralized identification and authentication mechanisms based on Decentralized...

Compact and Selective Disclosure for Verifiable Credentials

Self-Sovereign Identity SSI is a novel identity model that empowers individuals with full control over their data, enabling them to choose what information to disclose, with whom, and when. This paradigm is rapidly gaining traction worldwide, supported by numerous initiatives such as the European...

Next Generation Authentication for Data Spaces: an Authentication Flow Based on Grant Negotiation and Authorization Protocol for Verifiable Presentations (GNAP4VP)

Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization...

Verifiable Weighted Secret Sharing

Traditionally, threshold secret sharing TSS schemes assume all parties have equal weight, yet emerging systems like blockchains reveal disparities in party trustworthiness, such as stake or reputation. Weighted Secret Sharing WSS addresses this by assigning varying weights to parties, ensuring...

Chainless Apps: a Modular Framework for Building Apps with Web2 Capability and Web3 Trust

Modern blockchain applications are often constrained by a trade-off between user experience and trust. Chainless Apps present a new paradigm of application architecture that separates execution, trust, bridging, and settlement into distinct compostable layers. This enables app-specific sequencing...

A Novel Zero-Trust Identity Framework for Agentic AI: Decentralized Authentication and Fine-Grained Access Control

Traditional Identity and Access Management IAM systems, primarily designed for human users or static machine identities via protocols such as OAuth, OpenID Connect OIDC, and SAML, prove fundamentally inadequate for the dynamic, interdependent, and often ephemeral nature of AI agents operating at...

TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks

Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and potentially sensitive or private training data. So-called Zero-knowledge Succinct...

Verifying Differentially Private Median Estimation

Differential Privacy DP is a robust privacy guarantee that is widely employed in private data analysis today, finding broad application in domains such as statistical query release and machine learning. However, DP achieves privacy by introducing noise into data or query answers, which malicious...

Towards Verifiability of Total Value Locked (TVL) in Decentralized Finance

Total Value Locked TVL aims to measure the aggregate value of cryptoassets deposited in Decentralized Finance DeFi protocols. Although blockchain data is public, the way TVL is computed is not well understood. In practice, its calculation on major TVL aggregators relies on self-reports from...

SVAFD: a Secure and Verifiable Co-Aggregation Protocol for Federated Distillation

Secure Aggregation SA is an indispensable component of Federated Learning FL that concentrates on privacy preservation while allowing for robust aggregation. However, most SA designs rely heavily on the unrealistic assumption of homogeneous model architectures. Federated Distillation FD, which...