On the Effectiveness of Kernel-Level Evidence for Agent Security
cs.CR, cs.AI
Submitted: 2026-09-24
Updated: 2026-09-24
Comments: 53 pages, 2 figures, 28 tables, including appendices
Code: https://github.com/aquasecurity/tracee
License: http://creativecommons.org/licenses/by/4.0/
The gist: LLM agents are deployed into infrastructure that grants them broad host authority, yet existing agent-security benchmarks and defenses operate almost exclusively at the application telemetry layer:
Terminology
Abstract
LLM agents are deployed into infrastructure that grants them broad host authority, yet existing agent-security benchmarks and defenses operate almost exclusively at the application telemetry layer: the served tool manifest, the user prompt, and the model's messages. Some threats, however, smuggle malicious instructions and actions past the application boundary, leaving them invisible to that layer. In this work, we bridge that gap by pairing application-level agent telemetry with kernel-level syscall traces to present the first paired-evidence characterization of kernel-level versus application-layer signal for agent security. To quantify the value of the enhanced telemetry, we introduce Agent Cross-Layer Evidence (ACE), a paired-session corpus of 4,047 sessions and 17 threat models spanning six delivery-vector families and 14 of the 25 OWASP LLM and agentic threat categories, organized into 12 attack mechanics with per-mechanic characterization of where the most discriminative evidence lies. Across four distinct detector families, we find that kernel evidence is discriminative on its own and that composing it with application-layer evidence generally outperforms either single-layer view, revealing complementary signals that single-layer analyses can miss. We further demonstrate generalization to unseen attack families and transfer to an alternate agent runtime. Together, these findings establish the value of cross-layer evidence for agent security.
Sources
- FlowGuard: From Signals to Evidence for MCP Security Detection
- AgentBound: Securing Execution Boundaries of AI Agents
- Defeating Prompt Injections by Design
- QLoRA: Efficient Finetuning of Quantized LLMs
- StepShield: When, Not Whether to Intervene on Rogue Agents
- RedCode: Risky Code Execution and Generation Benchmark for Code Agents
- UNICORN: Runtime Provenance-Based Detector for Advanced Persistent Threats
- LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems
- Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
- ToolPRMBench: Evaluating and Advancing Process Reward Models for Tool-using Agents
- Quantifying Frontier LLM Capabilities for Container Sandbox Escape
- POIROT: Aligning Attack Behavior with Kernel Audit Records for Cyber Threat Hunting
- HOLMES: Real-time APT Detection through Correlation of Suspicious Information Flows
- TRACE: Trajectory Reasoning through Adaptive Cross-Step Evidence Aggregation for LLM Agents
- ChainWatch: A Kill Chain-Aligned Sequential Detection Framework for Multi-Step Attacks in MCP-Based AI Agent Systems
- TabICL: A Tabular Foundation Model for In-Context Learning on Large Data
- Identifying the Risks of LM Agents with an LM-Emulated Sandbox
- Toolformer: Language Models Can Teach Themselves to Use Tools
- Agent-Sentry: Bounding LLM Agents via Execution Provenance
- Training Deliberative Monitors for Black-Box Scheming Detection
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