Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
ToolTweak: An attack on tool selection in llm-based agents
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
years
2026 8roles
background 2polarities
background 2representative citing papers
DDIPE poisons LLM agent skills by embedding malicious logic in documentation examples, achieving 11.6-33.5% bypass rates across frameworks while explicit attacks are blocked, with 2.5% evading detection.
Over 99% of real-world SKILL.md files contain skill smells (violations of authoring best practices), and those smells almost never disappear as the skills evolve.
Five practical attacks on the x402 agentic payment protocol are demonstrated across authorization, binding, replay protection, and web handling, validated on local chains, Base Sepolia, live endpoints, and three open-source SDKs.
ShieldNet detects supply-chain poisoned tools in LLM agents by monitoring network interactions with a MITM proxy and lightweight classifier, reaching 0.995 F1 and 0.8% false positives on a new benchmark of 25+ attack types.
Adversarial compromise of tool outputs misleads agentic AI via breadth and depth attacks, revealing that epistemic and navigational robustness are distinct and often trade off against each other.
Tool-Guard uses isolated planning to quarantine suspicious tools, reducing tool description poisoning attacks on LLM agents while preserving task utility on AgentDojo and ASB benchmarks.
citing papers explorer
-
Measuring Real-World Prompt Injection Attacks in LLM-based Resume Screening
Roughly 1% of real resumes contain hidden prompt injections against LLM screeners, prevalence has risen over 1-2 years, and over 90% avoid explicit instructions.
-
Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems
DDIPE poisons LLM agent skills by embedding malicious logic in documentation examples, achieving 11.6-33.5% bypass rates across frameworks while explicit attacks are blocked, with 2.5% evading detection.
-
From Anatomy to Smells: An Empirical Study of SKILL.md in Agent Skills
Over 99% of real-world SKILL.md files contain skill smells (violations of authoring best practices), and those smells almost never disappear as the skills evolve.
-
Five Attacks on x402 Agentic Payment Protocol
Five practical attacks on the x402 agentic payment protocol are demonstrated across authorization, binding, replay protection, and web handling, validated on local chains, Base Sepolia, live endpoints, and three open-source SDKs.
-
ShieldNet: Network-Level Guardrails against Emerging Supply-Chain Injections in Agentic Systems
ShieldNet detects supply-chain poisoned tools in LLM agents by monitoring network interactions with a MITM proxy and lightweight classifier, reaching 0.995 F1 and 0.8% false positives on a new benchmark of 25+ attack types.
-
How Adversarial Environments Mislead Agentic AI?
Adversarial compromise of tool outputs misleads agentic AI via breadth and depth attacks, revealing that epistemic and navigational robustness are distinct and often trade off against each other.
-
Think Twice Before You Act: Protecting LLM Agents Against Tool Description Poisoning via Isolated Planning
Tool-Guard uses isolated planning to quarantine suspicious tools, reducing tool description poisoning attacks on LLM agents while preserving task utility on AgentDojo and ASB benchmarks.
- Poise: Position-Aware One-Instruction Skill Injection for Silent Execution on LLM Agents