mlsanityData to 14 Oct 2026

CAGE: Certified Authorization under Typed-Return Uncertainty for Tool-Using Agents

arXiv 2607.29190 ↗First posted 31 Jul 2026cs.AIcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Tool-using LLM agents act on typed tool returns, records pairing provenance and categorical fields with numerical values. Runtime permission gates generally authorize the observed return and action, leaving the decision unprotected against small errors in how the return was bound to its source. We ask whether a candidate action stays authorized over a declared neighborhood of plausible correctly bound returns: one admissible binding fault plus bounded numerical drift. We prove that certifying the categorical and numerical channels separately does not compose: perturbations that are safe on each channel alone can jointly turn the same action unsafe. CAGE certifies this joint neighborhood directly, enumerating the discrete branches exactly and certifying the continuous perturbation within each branch. Across synthetic, policy-as-code, regulatory, and real-transaction settings, CAGE removes the in-budget false allows that accurate pointwise gates admit, while keeping a useful fraction of decisions autonomous. When the policy is executable, CAGE-Exact certifies the policy itself; otherwise CAGE-Lip and CAGE-RS certify a learned gate under an explicit, measured fidelity assumption.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 23 of its 46 references that are in the map. First posted on arXiv 31 Jul 2026.

Builds on

23 of 46 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation9 Jun
  2. 2026Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode4 Apr
  3. 2026A Framework for Formalizing LLM Agent Security19 Mar
  4. 2025CommandSans: Securing AI Agents with Surgical Precision Prompt Sanitization9 Oct
  5. 2025SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems9 Jun
  6. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
  7. 2025Prompt Flow Integrity to Prevent Privilege Escalation in LLM Agents17 Mar

Other references · 16

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2019Certified Adversarial Robustness via Randomized Smoothingcited by 1,340
  3. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 493
  4. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 445
  5. 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 375
  6. 2024Defending Against Indirect Prompt Injection Attacks With Spotlightingcited by 267
  7. 2018Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networkscited by 195
  8. 2019Certified Robustness to Adversarial Word Substitutionscited by 168
  9. 2021Orthogonalizing Convolutional Layers with the Cayley Transformcited by 105
  10. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  11. 2019Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagationcited by 88
  12. 2019Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifierscited by 65
4 more
  1. 2020Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and Morecited by 56
  2. 2026Dive into Claude Code: The Design Space of Today's and Future AI Agent Systemscited by 38
  3. 2025An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architecturescited by 8
  4. 2025One Sample is Enough to Make Conformal Prediction Robustcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

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  1. 2026What Can Be Enforced? A Theory of Certified Runtime Safety for Tool-Using Agents
  2. 2026Reason Less, Verify More: Deterministic Gates Recover a Silent Policy-Violation Failure Mode in Tool-Using LLM Agents
  3. 2026Agent-Sentry: Bounding LLM Agents via Execution Provenance
  4. 2026AgentSecBench: Measuring Prompt Injection, Privacy Leakage, and Tool-Use Integrity in LLM Agents
  5. 2026Ghost in the Context: Policy-Carriage Integrity in LLM Agents
  6. 2026PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
  7. 2026Goal-Autopilot: A Verifiable Anti-Fabrication Firewall for Unattended Long-Horizon Agents
  8. 2026Trustworthy Agentic AI Requires Deterministic Architectural Boundaries