mlsanityData to 14 Oct 2026

Alignment Contracts for Agentic Security Systems

arXiv 2605.00081 ↗First posted 30 Apr 2026cs.CR cs.LOcited by 1

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

Abstract (arXiv)

Agentic security systems increasingly combine LLM planners with tools that can discover, validate, and report vulnerabilities. This creates an asymmetric control problem: the system should retain strong offensive capability inside an authorized engagement, while the same capabilities must be denied outside scope. Existing guardrails provide useful policy controls, but they do not make this boundary a first-class formal contract over observable effects. We introduce alignment contracts, a framework for specifying and enforcing behavioral constraints over observable effect traces. A contract defines scope, allowed and forbidden effects, resource budgets, and disclosure policies. We give the language finite-trace semantics, characterize satisfaction as a safety property with finite violation witnesses, develop refinement and one-way composition rules for modular contract engineering, and show that admissibility checking is decidable. We instantiate the framework for web-focused agentic security workflows and show how the same structure extends to other effect profiles. Under an explicit Effect Observability Assumption, where all SigmaEff-effects are mediated, the soundness theorem quantifies over the agent model and gives guarantees for mediated SigmaEff-effects, including enforcement soundness for monitor-realized traces. We also state an assumption-lifted adaptation result and formalize limits through undecidability transfer and observability-boundary theorems. A Lean 4 artifact checks the formal core theorems used by the paper.

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 10 of its 45 references that are in the map. First posted on arXiv 30 Apr 2026.

Builds on

10 of 45 references.

Closest ancestors · 7

Same direction, newest first

  1. 2025Enforcing Temporal Constraints for LLM Agents25 Dec
  2. 2025AgentGuard: Runtime Verification of AI Agents28 Sep
  3. 2025Multi-Agent Penetration Testing AI for the Web28 Aug
  4. 2025AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents24 Mar
  5. 2025Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents27 Feb
  6. 2025Firewalls to Secure Dynamic LLM Agentic Networks3 Feb
  7. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar

Other references · 3

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  2. 2024Cedar: A New Language for Expressive, Fast, Safe, and Analyzable Authorization (Extended Version)cited by 34
  3. 2026EVMbench: Evaluating AI Agents on Smart Contract Securitycited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls
  2. 2026AgentFlow: A Flow-Centric Policy Language and Framework for Securing LLM Agent Systems
  3. 2026ETAS: An Effect-Typed Language for Agent Systems
  4. 2026Boundary-State Control for Tool-Using Language-Model Agents: Commit-Time Consistency under State Drift
  5. 2026A Framework for Formalizing LLM Agent Security
  6. 2026AgentTrace: A Structured Logging Framework for Agent System Observability
  7. 2026Formal Policy Enforcement for Real-World Agentic Systems
  8. 2025Breaking and Fixing Defenses Against Control-Flow Hijacking in Multi-Agent Systems