{ "schema_version": 1, "builder_plan": { "scenario": "An agent reads external documents or tool responses that may contain malicious instructions.", "change": "Replay attacks and legitimate lookalikes in an isolated copy of your agent workflow.", "baseline": "Your existing checks with the same tasks, permissions, and tool responses.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Successful attacks missed, legitimate actions blocked, detection delay, and review cost.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2609.37196", "title": "ToolFence: Fine-Grained Authorization for Secure Tool-Using LLM Agents", "summary": "ToolFence replaces content-based input filters and multi-path consensus defenses (and offers a lower-latency alternative to data-flow control systems like CaMeL) by compiling a typed authorization blueprint before execution, enforcing it with a deterministic monitor, and escalating only missing capabilities to a judge model. It costs the engineering effort of defining typed blueprints and provenance tracking per tool, a small runtime overhead from the monitor, occasional judge-model calls, and a measured 3.80-point utility loss on clean tasks. It can fail when blueprints are incomplete or mis-specified for the production tool set, when the judge incorrectly grants a maliciously requested capability, and its results are validated only on AgentDojo with a frontier model, so transfer to other workloads is unverified.", "rationale": "The defense addresses a real production risk (indirect prompt injection, including within-tool argument manipulation) using only API-accessible components, but its security and utility trade-offs must be validated against the reader's own tools, blueprints, and threat model before production adoption.", "technique": "Provenance-aware capability authorization for tool-using agents", "family": "seguranca_e_guardrails", "recommendation": "testar", "published": "2026-09-29", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.37196", "independent_implementations": 0, "total_implementations": 1, "stars": 1, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "ToolFence replaces content-based input filters and multi-path consensus defenses (and offers a lower-latency alternative to data-flow control systems like CaMeL) by compiling a typed authorization blueprint before execution, enforcing it with a deterministic monitor, and escalating only missing capabilities to a judge model. It costs the engineering effort of defining typed blueprints and provenance tracking per tool, a small runtime overhead from the monitor, occasional judge-model calls, and a measured 3.80-point utility loss on clean tasks. It can fail when blueprints are incomplete or mis-specified for the production tool set, when the judge incorrectly grants a maliciously requested capability, and its results are validated only on AgentDojo with a frontier model, so transfer to other workloads is unverified.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.37196", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "On AgentDojo with Qwen3-max, ToolFence reduces overall attack success rate to near zero with a 3.80 percentage-point clean-utility drop; no multiplicative improvement factor is stated.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.37196", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [], "equations_status": "not_fetched", "core_kind": "", "equations_fetched_at": "", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }