# APPA: Recoverable Information-Flow Control for Real-World LLM Agents

arXiv: https://arxiv.org/abs/2607.24625 · HTML: https://mlsanity.com/paper/2607.24625/

## Key facts

- Cited by 1 papers 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 13 of its 36 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 13 of 13)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Defeating Prompt Injections by Design](https://mlsanity.com/paper/2503.18813/) (2025)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [ACE: A Security Architecture for LLM-Integrated App Systems](https://mlsanity.com/paper/2504.20984/) (2025)
- [From Agent Traces to Trust: A Survey of Evidence Tracing and Execution Provenance in LLM Agents](https://mlsanity.com/paper/2606.04990/) (2026)
- [Permissive Information-Flow Analysis for Large Language Models](https://mlsanity.com/paper/2410.03055/) (2024)
- [Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?](https://mlsanity.com/paper/2510.05244/) (2025)
- [MemLineage: Lineage-Guided Enforcement for LLM Agent Memory](https://mlsanity.com/paper/2605.14421/) (2026)
- [Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes](https://mlsanity.com/paper/2604.28138/) (2026)
- [DeltaBox: Scaling Stateful AI Agents with Millisecond-Level Sandbox Checkpoint/Rollback](https://mlsanity.com/paper/2605.22781/) (2026)
- [Optimizing Agent Planning for Security and Autonomy](https://mlsanity.com/paper/2602.11416/) (2026)
- [SafeHarbor: Defining Precise Decision Boundaries via Hierarchical Memory-Augmented Guardrail for LLM Agent Safety](https://mlsanity.com/paper/2605.05704/) (2026)

## Built on by (most cited first, 1 of 1)

- [Multi-Agent AI Safety as an Institutional Design Problem](https://mlsanity.com/paper/2608.09828/) (2026)

## Abstract (arXiv)

LLM agents deployed in practical workflows routinely mix private context, untrusted tool and web outputs, and external side effects. While information-flow control (IFC) provides structural defenses against prompt injection, data exfiltration, and confused-deputy attacks, conventional IFC relies on monotone taint tracking that either over-blocks benign operations or permanently strands downstream execution once an agent ingests unvetted data. We present APPA (Agentic Permissions Policy Algebra), which turns agent IFC from an abort-only barrier into a policy-governed recovery system. APPA enforces a dual-phase reference monitor at tool dispatch and protocol gateways (e.g., Model Context Protocol): before tool execution, it prospectively evaluates composite label restrictions and workflow history; upon completion, it validates realized outputs before context admission. For incremental rollout across unannotated tools, APPA incorporates gradual security typing with bounded cast resolution. To inspect untrusted data without poisoning primary agent context, APPA introduces on-demand trajectory confinement: disposable child branches absorb taint locally and exit through shape-bounded channels (attest-schema) with exact parent-label and transcript preservation, avoiding permanently partitioned multi-agent infrastructure. We prove core safety invariants: no-laundering gradual resolution, branch boundary isolation, and recovery containment against prompt-injected models. Across 6,600 controlled benchmark episodes spanning OWASP AgentThreatBench and enterprise workflows (Bench-Corp), APPA sustains 64.2-91% utility with zero observed attacks across 1,320 guarded episodes, establishing a practical defense for deployed tool-using agents.