Attacks and Mitigations for Distributed Governance of Agentic AI under Byzantine Adversaries

Agentic AI governance is a critical component of agentic AI infrastructure ensuring that agents follow their owner's communication and interaction policies, and providing protection against attacks from malicious agents. The state-of-the-art solution, SAGA, assumes a logically centralized point o...

Friday Squid Blogging: Squid in Byzantine Monk Cooking

This is a very weird story about how squid stayed on the menu of Byzantine monks by falling between the cracks of dietary rules. At Constantinople's Monastery of Stoudios, the kitchen didn't answer to appetite. It answered to the "typikon": a manual for ensuring that nothing unexpected happened a...

Protecting Context and Prompts: Deterministic Security for Non-Deterministic AI

Large Language Model LLM applications are vulnerable to prompt injection and context manipulation attacks that traditional security models cannot prevent. We introduce two novel primitives--authenticated prompts and authenticated context--that provide cryptographically verifiable provenance acros...

Zero-Trust Agentic Federated Learning for Secure IIoT Defense Systems

Recent attacks on critical infrastructure, including the 2021 Oldsmar water treatment breach and 2023 Danish energy sector compromises, highlight urgent security gaps in Industrial IoT IIoT deployments. While Federated Learning FL enables privacy-preserving collaborative intrusion detection,...

Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks

The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes...

EUVD-2023-2104

Malicious code in bioql PyPI...

EUVD-2025-0216

Malicious code in bioql PyPI...

EUVD-2023-2155

Malicious code in bioql PyPI...

On the Security and Privacy of Federated Learning: a Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions

Federated Learning FL is an emerging distributed machine learning paradigm enabling multiple clients to train a global model collaboratively without sharing their raw data. While FL enhances data privacy by design, it remains vulnerable to various security and privacy threats. This survey provide...

Bullshark on Narwhal: Implementation-Level Workflow Analysis of Round-Based DAG Consensus in Theory and Practice

Round-based DAGs enable high-performance Byzantine fault-tolerant consensus, yet their technical advantages remain underutilized due to their short history. While research on consensus protocols is active in both academia and industry, many studies overlook implementation-level algorithms, leavin...

Generalization under Byzantine and Poisoning Attacks: Tight Stability Bounds in Robust Distributed Learning

Whitepaper called Generalization Under Byzantine and Poisoning Attacks: Tight Stability Bounds In Robust Distributed Learning...

Self-Stabilizing Replicated State Machine Coping with Byzantine and Recurring Transient Faults

Whitepaper called Self-Stabilizing Replicated State Machine Coping With Byzantine And Recurring Transient Faults...

List-Decodable Byzantine Robust PIR: Lower Communication Complexity, Higher Byzantine Tolerance, Smaller List Size

Private Information Retrieval PIR is a privacy-preserving primitive in cryptography. Significant endeavors have been made to address the variant of PIR concerning the malicious servers. Among those endeavors, list-decodable Byzantine robust PIR schemes may tolerate a majority of malicious...

Byzantine Outside, Curious Inside: Reconstructing Data through Malicious Updates

Federated learning FL enables decentralized machine learning without sharing raw data, allowing multiple clients to collaboratively learn a global model. However, studies reveal that privacy leakage is possible under commonly adopted FL protocols. In particular, a server with access to client...

Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation

Federated Learning FL enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, wh...

EarthOL: a Proof-Of-Human-Contribution Consensus Protocol -- Addressing Fundamental Challenges in Decentralized Value Assessment with Enhanced Verification and Security Mechanisms

This paper introduces EarthOL, a novel consensus protocol that attempts to replace computational waste in blockchain systems with verifiable human contributions within bounded domains. While recognizing the fundamental impossibility of universal value assessment, we propose a domain-restricted...

Coded Robust Aggregation for Distributed Learning under Byzantine Attacks

In this paper, we investigate the problem of distributed learning DL in the presence of Byzantine attacks. For this problem, various robust bounded aggregation RBA rules have been proposed at the central server to mitigate the impact of Byzantine attacks. However, current DL methods apply RBA rul...

Kudzu: Fast and Simple High-Throughput BFT

We present Kudzu, a high-throughput atomic broadcast protocol with an integrated fast path. Our contribution is based on the combination of two lines of work. Firstly, our protocol achieves finality in just two rounds of communication if all but $p$ out of $n = 3f + 2p + 1$ participating replicas...

CVE-2025-24371

CometBFT is a distributed, Byzantine fault-tolerant, deterministic state machine replication engine. In the blocksync protocol peers send their base and latest heights when they connect to a new node A, which is syncing to the tip of a network. base acts as a lower ground and informs A that the...

Slashing Evasion Through Re-Delegation

github.com/cosmos/cosmos-sdk is vulnerable to slashing evasion during re-delegation. The vulnerability is due to an issue in the slashing mechanism that allows delegations contributing to byzantine behavior of a validator to evade slashing penalties through re-delegation, if the validator has not...