{ "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": "2610.12292", "title": "One Word Opens the Gate: The Option-Channel Attack on Typed Decision Models as Agent Guardrails", "summary": "The paper evaluates using open-weight typed decision models as the allow-or-block gate for tool calls and messages, replacing deterministic policy rules or human review; the reader should not adopt them as the deciding component. They cost an inference call per decision while providing no robust security gain, and the deterministic-rule alternative is cheaper and achieved 100% accuracy on the tested policies. They fail open under attacker-controlled context (irrelevant log lines, misleading option labels), all tested defenses are defeated, and confidence-based escalation fails because reversed decisions remain confident.", "rationale": "The evidence shows these guardrail gates are unreliable and trivially attackable, while a deterministic rule over parsed fields is strictly more accurate and cheaper, so adoption as a decision component is not justified.", "technique": "Typed decision models as agent guardrails", "family": "seguranca_e_guardrails", "recommendation": "nao_aplica", "published": "2026-10-08", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.12292", "independent_implementations": 1, "total_implementations": 2, "stars": 132, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "The paper evaluates using open-weight typed decision models as the allow-or-block gate for tool calls and messages, replacing deterministic policy rules or human review; the reader should not adopt them as the deciding component. They cost an inference call per decision while providing no robust security gain, and the deterministic-rule alternative is cheaper and achieved 100% accuracy on the tested policies. They fail open under attacker-controlled context (irrelevant log lines, misleading option labels), all tested defenses are defeated, and confidence-based escalation fails because reversed decisions remain confident.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.12292", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Allow-or-block accuracy of 36% to 72% against 50% chance on injection, jailbreak, and toxic-content screening; unrelated log text raises fail-open rate from 0% to 63%; relabeling the permissive option raises it to 93-100%; a deterministic rule over parsed typed fields reaches 100% on all six policies.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.12292", "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": "" }