Who is the 'I' in AI? Establishing Decentralized Identity and Accountability for Autonomous Agents
The rapid proliferation of autonomous AI agents in decentralized ecosystems challenges existing identity paradigms. Whil
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Cybersecurity evaluations of AI agents have focused largely on offense, yet a working exploit does not show that an agen
Unsupervised Data Drift Monitoring for Deployed Deepfake Speech Detectors
Deepfake speech detectors are typically evaluated as binary classifiers on fixed benchmarks, but deployed systems face e
StableJudge: A Lightweight Adversarial Defense Framework for LLM-as-a-Judge
Large language models (LLMs) are increasingly used as judges to assign scalar scores that drive downstream decision-maki
Logit Leakage through Floating-Point Bias Addition
Language model APIs can expose token-specific biases while withholding logits and token probabilities. Existing bias-bas
PrivacyGuard: Source-Linked Sanitization and Locally Resolvable PII References for Cloud GUI Agents
Cloud graphical user interface (GUI) agents send instructions and screenshots to remote planners, exposing personally id
CryptoFlowBench: Benchmarking Multi-Hop Reasoning on Blockchains for Cryptocurrency Fund Tracing
Pseudonymous accounts and complex smart-contract ecosystems substantially hinder the tracing of illicit cryptocurrency f
When Agents Talk, Secrets Travel: Topology-Driven Leakage and Recipient-Conditioned Sanitization in Multi-Agent LLM Systems
Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, si
AutoSec-RAN: Specification-Grounded LLM Fine-Tuning for Automated Security Analysis of O-RAN
This paper presents AutoSec-RAN, a specification-grounded large language model (LLM) framework for automated security tr
Revocation Is Not Complete at Receipt: An Executable Outcome Contract for Distributed Agent-Tool Gateways
Distributed agent workflows cache delegated authority at tool gateways. Revocation may be reported at issuer commit, tra
Hardware-Assisted Multi-Stage Ransomware Detection with Adaptive Machine Learning
Ransomware is a fast-escalating privacy threat causing significant financial and data losses worldwide. As attacks grow
Scalable Weak-to-Strong Verifier Hardening for Agent Environments
Agent environments are essential for large language model (LLM) development. They use programmatic verifiers to turn com
RetryBench: Separating Retry Permission from Recovery Outcomes in GPT Tool Use
False retry permission (FRP) occurs when an agent retries a timed-out tool call without support from the visible contrac
What the Model Cannot See: Covert and Provable Text Invisibility in LLMs
Text wrapped in a trigger can be made behaviorally invisible to a language model: omitted from summaries and quotations,
SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verification
Agent skills encode operational procedures and domain expertise as reusable knowledge assets, yet their distribution in
Security in LLM-Assisted Coding: A Systematic Literature Review
Large Language Models accelerate software development but introduce severe security vulnerabilities. This systematic rev
Accuracy and Uncertainty in Machine Deception Detection (Research White Paper)
A deception detector is only as useful as the trust its alarms deserve. Like a polygraph, detectors that read a language
Exploring Jailbreak Vulnerability Across Depth-Based Dynamic Inference in Large Language Models
Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in whi
Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems
Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans. These
VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails
LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can