Boundary-Aware Test-Time Adaptation for Zero-Shot Medical Image Segmentation

test-time adaptation. This framework integrates two key mechanisms: (1) The encoder-level Gaussian prompt injection embeds Gaussian-based prompts directly into the image encoder, providing explicit guidance for initial

Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models

Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production, guardrails must mitigate these attacks under strict low-latency constraints

MIRROR: Novelty-Constrained Memory-Guided MCTS Red-Teaming for Agentic RAG

Multimodal agentic retrieval-augmented generation (RAG) systems expand the attack surface beyond prompt injection to include text poisoning, image injection, direct-query attacks, and orchestrator-level tool manipulation. Existing

Paraphrasing Adversarial Attack on LLM-as-a-Reviewer

growing attention, making it essential to examine their potential vulnerabilities. Prior attacks rely on prompt injection, which alters manuscript content and conflates injection susceptibility with evaluation robustness. We propose

What Matters For Safety Alignment?

services, highlighting an urgent need for architectural and deployment safeguards. Fourth, roleplay, prompt injection, and gradient-based search for adversarial prompts are the predominant methodologies for eliciting unaligned behaviors

PromptScreen: Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline

Prompt injection and jailbreaking attacks pose persistent security challenges to large language model (LLM)-based systems. We present PromptScreen, an efficient and systematically evaluated defense architecture that mitigates these threats

Penetration Testing of Agentic AI: A Comparative Security Analysis Across Models and Frameworks

functionality of a university information management system and 13 distinct attack scenarios that span prompt injection, Server Side Request Forgery (SSRF), SQL injection, and tool misuse. Our 130 total test

The Target Agent Is Its Own Best Critic but a Poor Proposer: Self-Improved Red Teaming

large language models (LLMs) to iteratively diagnose failed attacks and propose improved adversarial prompts or injections. Although prior work strengthens this loop through prompt engineering and search-harness design

GhostEI-Bench: Do Mobile Agents Resilience to Environmental Injection in Dynamic On-Device Environments?

inter-app interactions, exposes them to a unique and underexplored threat vector: environmental injection. Unlike prompt-based attacks that manipulate textual instructions, environmental injection corrupts an agent's visual perception

Trojan's Whisper: Stealthy Manipulation of OpenClaw through Injected Bootstrapped Guidance

stealthy attack vector that embeds adversarial operational narratives into bootstrap guidance files. Unlike traditional prompt injection, which relies on explicit malicious instructions, guidance injection manipulates the agent's reasoning context

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code

integration. We produce payloads in two settings: using prompts that specify only functional requirements, and using prompts that inject a structured history of prior outcomes to force divergence. We measure

The Infinite Mutation Engine? Measuring Polymorphism in LLM-Generated Offensive Code

integration. We produce payloads in two settings: using prompts that specify only functional requirements, and using prompts that inject a structured history of prior outcomes to force divergence. We measure

Open WebUI: Sharing models for others to use (read permission

Agent Data Injection Attacks are Realistic Threats to AI Agents

user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security has primarily focused on indirect prompt injection (IPI). Its most well

ChronoState: Hidden Elapsed-Time Conditioning for Temporal-State Action Selection in Frozen-Backbone Language Models

does not establish autonomous time tracking, broad unseen-family abstraction, or superiority over prompt-injected timestamps

@dynatrace-oss/dynatrace-mcp-server has a DQL injection via parameters not

Imperceptible Jailbreaking against Large Language Models

imperceptible jailbreaks achieve high attack success rates against four aligned LLMs and generalize to prompt injection attacks, all without producing any visible modifications in the written prompt. Our code

Verifying Intent and Harm: A Unified Defense Against LLM-Generated Threats

conflict resolution. We formalize a threat model for prompt-response attacks and evaluate the framework across five threat categories: jailbreaks, prompt injection, phishing, cyber abuse, and harmful content. Experiments

BenchOverflow: Measuring Overflow in Large Language Models via Plain-Text Prompts

large language models (LLMs) in which plain-text prompts elicit excessive outputs, a phenomenon we term Overflow. Unlike jailbreaks or prompt injection, Overflow arises under ordinary interaction settings

ProjGuard: Safety Monitoring for Computer-Use Agents via Low-Dimensional Projections

real operating systems, but this capability has also increased the risks posed by prompt injection, indirect instructions, and visual attacks. Existing defenses typically rely on analyzing the prompt or each