JAW uses hybrid program analysis to evolve inputs that hijack agentic workflows, successfully compromising 4714 GitHub workflows and eight n8n templates to enable actions like credential exfiltration.
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Defending against indirect prompt injection attacks with spotlighting
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UNVERDICTED 20representative citing papers
ShadowMerge poisons graph-based agent memory via relation-channel conflicts using an AIR pipeline, achieving 93.8% average attack success rate on Mem0 and three real-world datasets while bypassing existing defenses.
Heimdallr detects LLM-induced security risks in GitHub CI workflows by normalizing them into an LLM-Workflow Property Graph and combining triggerability analysis with LLM-assisted dataflow summarization, achieving over 0.91 F1 on threat detection in evaluation.
Trojan Hippo attacks on LLM agent memory achieve 85-100% success rates in data exfiltration across four memory backends even after 100 benign sessions, while evaluated defenses reduce success rates but impose varying utility costs.
AgentDojo introduces an extensible evaluation framework populated with realistic agent tasks and security test cases to measure prompt injection robustness in tool-using LLM agents.
The TAB benchmark reveals that frontier terminal agents achieve high task completion but low selective alignment with relevant environmental cues over distractors, and prompt-injection defenses block both.
IPI-proxy is a toolkit using an intercepting proxy to inject indirect prompt injection attacks into live web pages for testing AI browsing agents against hidden instructions.
BOA uses budgeted search over agent trajectories to report the probability an LLM agent stays safe, finding unsafe paths that sampling misses.
AgentVisor cuts prompt injection success rate to 0.65% in LLM agents with only 1.45% utility loss via semantic privilege separation and one-shot self-correction.
Seven cross-domain techniques for prompt injection detection are proposed; three implemented versions raise F1 scores on multiple benchmarks while releasing all code and data.
The paper defines causality laundering as an attack leaking information from denial outcomes in LLM tool calls and proposes the Agentic Reference Monitor to block it using denial-aware provenance graphs.
AgentShield uses layered deception traps in LLM agent tool interfaces to detect indirect prompt injection compromises with 90.7-100% success on commercial models, zero false positives, and cross-lingual transfer without retraining.
ARGUS defends LLM agents from context-aware prompt injections by tracking information provenance and verifying decisions against trustworthy evidence, reducing attack success to 3.8% while retaining 87.5% task utility.
Semia synthesizes Datalog representations of agent skills via constraint-guided loops to enable reachability queries for semantic risks, finding critical issues in over half of 13,728 real skills with 97.7% recall on expert-labeled samples.
Spore extracts private data from LLM memory with one query in black-box mode or ranked tokens in gray-box, outperforming prior attacks while bypassing defenses.
GAAP guarantees confidentiality of private user data for AI agents by enforcing user-specified permissions deterministically through persistent information flow tracking, without trusting the agent or requiring attack-free models.
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.
QuickScope uses modified COUP Bayesian optimization to find truly difficult questions in dynamic LLM benchmarks more sample-efficiently than baselines while cutting false positives.
Only output filtering with hardcoded rules in application code prevented prompt injection leaks in LLMs, as all model-based defenses were defeated by an adaptive attacker.
A domain-specific multi-layer safeguard for educational LLM tutors achieves 0% false positives and 46.34% attack bypass at 2.5 ms latency on a 480-query holdout, outperforming NeMo Guardrails in usability but not fully in robustness.
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