AI Threat AlertPractical and Stealthy Touch-Guided Jailbreak Attacks on Deployed Mobile Vision-Language Agents
safety alignment of LVLMs. Moreover, we developed three representative Android applications and curated a prompt-injection dataset for mobile agents. We evaluated our attack across multiple LVLM backends, including closed
SwitchCraft: Training-Free Multi-Event Video Generation with Attention Controls
training-free framework for multi-event video generation. Our key insight is that uniform prompt injection across time ignores the correspondence between events and frames. To this end, we introduce
SwitchCraft: Training-Free Multi-Event Video Generation with Attention Controls
training-free framework for multi-event video generation. Our key insight is that uniform prompt injection across time ignores the correspondence between events and frames. To this end, we introduce
SIR: Self-improving Red-teaming for Compute Use Agents
they can be exposed to untrusted content while operating, they are vulnerable to indirect prompt injection (IPI), in which an adversary plants instructions in content the agent will read
Detecting Malicious Agent Skills in the Wild using Attention
foothold, which turns the skill marketplace into a new attack surface for agentic systems. Prompt-injection defenses do not carry over to this setting. They rely on a boundary between
Reasoning Up the Instruction Ladder for Controllable Language Models
inputs and predefined higher-priority policies, our trained model enhances robustness against jailbreak and prompt injection attacks, providing up to a 20% reduction in attack success rate (ASR). These results
Surviving the Router: Optimizing Skill Injections for Retrieval and Execution
skill routers to execute complex tasks. While recent studies highlight the threat of prompt injections embedded in these skills, existing evaluations often assume settings where the malicious skill is already
Mission-Level Runtime Assurance for LLM-Assisted ISR Swarms over a Verification-Aware Fabric
than reported as mission-wide all-clears. On a simulated ISR mission, an indirect prompt injection that causes real LLM planners to split a prohibited collection task across four platforms
CHAI: Command Hijacking against embodied AI
this paper, we introduce CHAI (Command Hijacking against embodied AI), a physical environment indirect prompt injection attack that exploits the multimodal language interpretation abilities of AI models. CHAI embeds deceptive
How Reliable Is Your Jailbreak Judge? Calibration and Adversarial Robustness of Automated ASR Scoring
Almost every paper on LLM jailbreaks and prompt injection reports an attack-success rate (ASR), and that number is assigned not by people but by an automated judge: either
Invisible Threats from Model Context Protocol: Generating Stealthy Injection Payload via Tree-based Adaptive Search
explored attack surface, specifically the malicious manipulation of tool responses. Existing techniques for indirect prompt injection that target MCP suffer from high deployment costs, weak semantic coherence, or heavy white
Sirens' Whisper: Inaudible Near-Ultrasonic Jailbreaks of Speech-Driven LLMs
case study, the underlying covert acoustic channel enables a broader class of high-fidelity prompt-injection and commandexecution attacks
Overeager Coding Agents: Measuring Out-of-Scope Actions on Benign Tasks
call these scope expansions overeager actions, an authorization problem distinct from capability failures, prompt injection, or sandbox escapes. We present OverEager-Gen, a benchmark dedicated to overeager behavior on benign
Black-box Optimization of LLM Outputs by Asking for Directions
general method to three attack scenarios: adversarial examples for vision-LLMs, jailbreaks and prompt injections. Our attacks successfully generate malicious inputs against systems that only expose textual outputs, thereby dramatically
SecureCAI: Injection-Resilient LLM Assistants for Cybersecurity Operations
triage, and malware explanation; however, deployment in adversarial cybersecurity environments exposes critical vulnerabilities to prompt injection attacks where malicious instructions embedded in security artifacts manipulate model behavior. This paper introduces
Certified Execution Harness for Agentic AI: Safe and Least-Intrusive Petri-Net Supervisory Control Between Reasoning and Action
studies the problem of safe tool execution for large language model (LLM) agents under prompt injection. Using Petri nets, we formalize the execution harness between the LLM and its tools
The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail?
We prove that no continuous, utility-preserving wrapper defense-a
From Transcripts to AI Agents: Knowledge Extraction, RAG Integration, and Robust Evaluation of Conversational AI Assistants
call coverage, factual accuracy, and human escalation behavior. Additional red teaming assesses robustness against prompt injection, out-of-scope, and out-of-context attacks. Experiments are conducted in the Real
LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges
that limit current assessment practices. Beyond performance metrics, we identify emerging robustness risks, including prompt injection, data poisoning, retrieval vulnerabilities, and reward hacking, which expose automated review pipelines to strategic
AprielGuard
Existing moderation tools often treat safety risks (e.g. toxicity, bias) and adversarial threats (e.g. prompt injections, jailbreaks) as separate problems, limiting their robustness and generalizability. We introduce AprielGuard