Oracle Poisoning corrupts knowledge graphs used by AI agents via tool calls, leading tested models to accept fabricated claims at 100% under directed queries in a production-scale demonstration.
hub
Securing AI agents with information-flow control
17 Pith papers cite this work. Polarity classification is still indexing.
17
Pith papers citing it
hub tools
years
2026 17representative citing papers
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.
MCPHunt benchmark finds 11.5-41.3% policy-violating credential propagation in multi-server MCP agents across five models, reducible up to 97% by prompt mitigations while retaining most utility.
NeuroTaint is the first taint tracking framework for LLM agents that uses offline auditing of semantic, causal, and persistent context to detect flows from untrusted sources to privileged sinks.
TRUSTDESC prevents tool poisoning in LLM applications by automatically generating accurate tool descriptions from code via a three-stage pipeline of reachability analysis, description synthesis, and dynamic verification.
PACT achieves perfect security and utility under oracle provenance by enforcing argument-level trust contracts based on semantic roles and cross-step provenance tracking, outperforming invocation-level monitors in AgentDojo evaluations.
A parameterized DFA firewall enforces safe tool sequences for structured AI agents, reducing attack success rates to 2.2% in tested workflows with low added latency.
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.
Sleeper channels enable persistent prompt injection in always-on AI agents via persistence substrate and firing separation, countered by provenance gates using action digests and owner attestations with a soundness theorem.
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.
Alignment contracts define scope, allowed effects, budgets and disclosure rules as safety properties over finite effect traces, with decidable admissibility, refinement rules, and Lean-verified soundness under an observability assumption.
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.
AI agents should shift from on-the-fly plan synthesis to invoking pre-engineered, tested, and reusable workflows stored in an AI Workflow Store to gain reliability and security.
Symbolic guardrails enforce 74% of specified safety policies in agent benchmarks and boost safety without hurting utility.
citing papers explorer
-
Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility
Symbolic guardrails enforce 74% of specified safety policies in agent benchmarks and boost safety without hurting utility.