AI Threat AlertAdvSim2Real : Training Web Agents Against Adaptive Prompt Injection in a Web World Model
Web agents complete user requests by reading and acting on
Secure Speculative Decoding for Large Language Models
disproportionately high cost to security, with attack success rates for jailbreak and prompt injection attacks increasing much faster than utility degrades. We then propose SecureSD, a new theory-guided speculative
Towards a Unified Misuse Monitoring Benchmark
sources: decomposition attacks, where a harmful request is split into innocuous sub-requests, and prompt injection attacks, where a compromised tool delivers a malicious instruction. Existing evaluations treat these threats
TrustMI: Causally controlling how assistants trust their users
whether this effect extends to several safety-related agent settings involving harmful requests, prompt injections, and insider threats, while using benign-task and reasoning as controls. Our findings provide evidence
Aletheia: Permission-Minimality Testing for Coding-Agent Rules
Repository instruction files guide coding agents, but also expose them to prompt injection. Malicious rules can request credential access or data transfer while the agent produces a correct patch
Towards Efficient HPC Systems for Agents: Challenges and Opportunities
bandwidth-provisioned filesystems, repeated rediscovery of what earlier sessions already learned, and new prompt-injection and policy-enforcement surfaces. Neither banning agents nor treating them as ordinary users is sustainable
ToolFence: Fine-Grained Authorization for Secure Tool-Using LLM Agents
Tool-using LLM agents remain vulnerable to indirect prompt injection because trusted instructions and untrusted observations share one context, allowing malicious content to steer consequential input-filtering defenses. Multi-path
Selecting The Most Informative Tokens in Natural Language Autoencoders
understand a potential threat? We study this question across $4.7$ million explanations on prompt injection and concealment. We compare signals from model computation with a ranker trained only on chat
Tracekit: Tamper-Evident Intent-Reasoning-Action Auditing for Autonomous Coding Agents
real Claude Code runs, the agent never acted on four planted indirect prompt injections and disclosed each. The provider withheld the text of all 27 thinking blocks, so self-report
Nudgeability: Reasoning Models Follow Confidence Signals Without Tracking Their Own Competence
separate predictor), how it is presented to the model (numerical prediction, confidence token, prompt injection), and whether it changes the model's subsequent action. We isolate the third question
Certified Multi-Source Integrity for Structured Agent Actions
critical fields in documents and tool outputs that an adversary can corrupt, and indirect prompt injection can drive the model itself to extract attacker-chosen values. Current defenses gate
When Consent Outlives Context: Residual Authority Replay in Long-Lived Agents
demonstrate that residual-authority replay arises in practice and substantially increases the success of prompt-injection and context-rebinding attacks. We evaluate 508 AgentDojo attack cases across six LLM families
Evaluating System One Models for Agent Security Decisions: Reliability, Calibration, and Selective Automation
Model-based judges support agent security by detecting prompt injections, assessing interaction risks, and screening harmful requests. System One models expose typed decisions with probabilities that software
Instrumental Monitor Evasion Emerges Under Ordinary Task Pressure
with overrefusal, as it frequently abandons otherwise solvable tasks under a denial-of-service prompt injection. Our findings show that ordinary task pressure can lead to adaptive attempts to evade
ENDOPROMPT: Victim-Side Pseudo-References for Utility Degradation
Prompt injection can degrade benign task performance without eliciting harmful content. Yet many attack objectives depend on task labels or predefined target responses. We present ENDOPROMPT, a white-box method
OllamaDrama: Designing and Deploying a Honeypot to Measure Attacks on Exposed LLM Infrastructure
abuse, path traversal and SSRF probes, RCE and cryptocurrency mining payloads, resource exhaustion attempts, prompt injection, information extraction, and agent-oriented tool use. Our results provide empirical insight into real
Through Human Eyes and Machine Eyes: Understanding View Mismatch in Video See-Through Extended Reality
tasks. Through four representative case studies, we illustrate potential risks and failure modes including prompt injection, privacy leakage, human-invisible information bias, and missing human-visible information. Our results show
ClaimMirage: When Self-Claims in Domain Names Change LLM Threat Judgments
change how a large language model (LLM) judges a domain name, without explicit prompt-injection commands. We study this manipulation as ClaimMirage: a name under inspection claims its own safety
Ajar: Measuring Open Privilege in Agent Defenses
that boundary. Today these techniques are evaluated on agent-security benchmarks built around indirect prompt injection. Those benchmarks judge a defense by how far it brings the number of successful
Universal Fractal Natural Language Decision Map: Real-Time Edge Triage Across Heterogeneous Domains
Acoustic Damping Filter grounded in token entropy and phonetic spectral density that insulates against prompt injections (0.0% empirical bypass; 95% Wilson CI: [0.0%, 30.8%]) while pruning escape iterations