AI Threat Alert
Insurance of Agentic AI
independently generating insured events through external actions. We analyze major risk pathways, including hallucinations, prompt-injection attacks, autonomous decision errors, model drift, dependency failures, and cyber-physical harms, and evaluate
Hybrid Adversarial Defence for Natural Language Understanding Tasks
similar adversarial robustness from our hybrid model (up to 57.14\% improvement in accuracy). For prompt injection (SafeGuard) and jailbreak detection (AdvBench, DAN) datasets our hybrid model is also very strong
AgentRedBench: Dynamic Redteaming and Integration-Aware Defense for LLM Agents over SaaS Integrations
Indirect prompt injection in tool-use agents is a concrete production threat: LLM agents read from integrations (third-party services such as Gmail, Salesforce, or Jira accessed through tool calls
Token-Level Generalization in LoRA Adapter Backdoors: Attack Characterization and Behavioral Detection
training data poisoning while preserving baseline task performance. On a Qwen 2.5 1.5B prompt-injection classifier, a small fraction of poisoned examples drives a clean-accuracy-preserving backdoor
LACUNA: Safe Agents as Recursive Program Holes
would also sharpen safety problems. A model can be diverted by a prompt injection, call the wrong tool, or fail partway and leave an inconsistent state, and each such failure
Towards Demystifying and Repairing LLM-in-the-Loop Vulnerabilities
Loop vulnerabilities are more challenging to precisely fix, especially for those involving prompt injections where the Pass@1 rate is only
Aligning Provenance with Authorization: A Dual-Graph Defense for LLM Agents
must read external data sources (emails, webpages, files) that attackers can control; through indirect prompt injection, attackers embed malicious instructions in this data to manipulate agents into performing unauthorized operations
AgentSecBench: Measuring Prompt Injection, Privacy Leakage, and Tool-Use Integrity in LLM Agents
LLM agents process trusted instructions, retrieved records, and tool observations
How Agentic AI Coding Assistants Become the Attacker's Shell
attacker's shell to run unauthorized commands. In this article, we examine how these prompt injection attacks work, measure their prevalence, discuss the limitations and challenges of current defenses
Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents
whether an agent completes a single scenario safely, often under adversarial conditions such as prompt injection or memory poisoning. In deployment, however, a single agent serves many independent tasks over
ADR: An Agentic Detection System for Enterprise Agentic AI Security
baselines (ALRPHFS, GuardAgent, LlamaFirewall) by 2--4x in F1-score. On AgentDojo (public prompt injection benchmark), ADR detects all attacks with only three false alarms out of 93 tasks
Web Agents Should Adopt the Plan-Then-Execute Paradigm
into the model when deciding on the next action, creating a direct path for prompt injections to steer the agent's control flow. Plan-then-execute changes this boundary: untrusted
Sleeper Channels and Provenance Gates: Persistent Prompt Injection in Always-on Autonomous AI Agents
Always-on AI agents (OpenClaw, Hermes Agent) run as a
No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills
ignores the documented constraint. These violations are invisible to static analyzers, traditional fuzzers, and prompt-injection defenses alike, yet they undermine the very contract a user trusts when installing
No More, No Less: Task Alignment in Terminal Agents
Bench agent achieves high task completion but low task alignment on TAB. Evaluating six prompt-injection defenses further shows that suppressing distractor execution also suppresses the cues required for task
Agents Should Replace Narrow Predictive AI as the Orchestrator in 6G AI-RAN
edge quantization, neuro-symbolic verification to curb hallucinations, and securing orchestration frameworks against adversarial prompt injections
RUBEN: Rule-Based Explanations for Retrieval-Augmented LLM Systems
safety, specifically to test the resiliency of safety training and effectiveness of adversarial prompt injections
Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning
query at runtime via tool-use protocols, causing incorrect conclusions through correct reasoning. Unlike prompt injection, Oracle Poisoning manipulates the data agents reason over, not their instructions. We demonstrate
Constraining Host-Level Abuse in Self-Hosted Computer-Use Agents via TEE-Backed Isolation
legitimately deployed agent may be steered toward unsafe operations through malicious messages, indirect prompt injection, unsafe skills, or tampering along the host-side control path. We argue that such risks
Laundering AI Authority with Adversarial Examples
produces confident and authoritative responses about the \emph{wrong} input. Unlike jailbreaks or prompt injections, our attacks do not compromise model alignment; the attack operates entirely at the perceptual level