Security & Cryptography · Computer Science & AI
Cryptography & Security
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1,372 papers, from 4 September 2026 to 5 November 2026: 1,372 arXiv preprints.
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Newest papers, page 44
Proof-Gated Signing: Solver-Checked Transaction Guards that Hold Under State Drift for Onchain AI Agents
AI agents that control wallets read attacker-reachable content, so they can be steered into proposing harmful transactions.Deny Without Disabling: Authorization-Paired Evaluation and Control for Multi-Agent Systems
Multi-agent systems derive their capabilities from sharing evidence, delegating tasks, and combining information across agents.On the Relationship between Model Quantization and Model Inversion Attacks
Model quantization reduces the numerical precision of neural network weights and activations to lower storage and computational costs.Representation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents
Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or states in isolation.Quantum Secret Sharing and Error Correction vs No-Cloning
Secret sharing is ubiquitous throughout cryptography.Blind Unforgeability implies Plus-One Unforgeability
We prove that quantum blind-unforgeability implies quantum plus-one unforgeability, settling an open question in the literature.No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents
Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks.Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability to generalize as network scale changes.Backdoor Containment via Expert Quarantine and Shutdown in LLMs
Backdoored large language models (LLMs) can behave normally on benign inputs while producing attacker-specified outputs under hidden triggers.Backdoor Purification for LoRA-Tuned LLMs via Null-Space Projection
With the rapid adoption of large language models (LLMs) and parameter-efficient fine-tuning (PEFT) methods, the risk of backdoor attacks has become more severe.Progressive-Resolution Secure Aggregation for Federated Learning
Secure aggregation lets a server recover an aggregate of client updates without observing any individual update, but conventional protocols fix the aggregate precision when clients upload.Crossing the Cyber Divide: Sim-to-Sim and Sim-to-Real Transfer for RL Agents
Cyber attack agents are typically trained and evaluated within a single simulator, making it unclear whether learned policies transfer beyond the environments in which they were developed.Sapien: A Stateful Policy Engine for Autonomous AI Agents
Contextual security defenses prevent AI agents from taking rogue actions by synthesizing a task-specific policy and enforcing it on the agent's tool calls.FinRT: Distilling Adaptive Red-Teaming Strategies into Reusable Adversarial Generators in Consumer Finance
In regulated industries like consumer finance, seemingly harmless user queries can exploit large language model vulnerabilities, triggering safety failures and pushing responses dangerously close to…CounterSteer: Suppressing Indirect Prompt Injection with Activation Steering
Indirect prompt injection makes an LLM agent treat untrusted retrieved text as instructions.Understanding Private Evolution as Learning-Augmented Clustering
Private Evolution (PE) is a differentially private algorithm for synthetic data generation.Does the Unsafe Gradient Survive a Conversation? On the Fragility of Gradient-Based Jailbreak Detection in Multi-Turn Dialogue
Safety-aligned language models are commonly deployed as multi-turn assistants, which lets adversaries spread unsafe intent across several user turns instead of a single prompt.Safer Content or Firmer Refusals? A Hybrid Perturbation Defense for Alignment under Harmful Fine-tuning
Fine-tuning-as-a-service lets users adapt a safety-aligned language model to their own data, but it also creates a harmful fine-tuning attack surface: a small amount of harmful data mixed into an…SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs
As LLM-based agents perform increasingly complex tasks, Agent Skills have emerged as a flexible mechanism for extending their capabilities.Controlled Decoding Attacks on Black-Box LLMs
Manipulating next-token probabilities during generation can bypass the safety alignment of large language models.