Warn when configured LLM is not frontier-recommended (#586)

Co-authored-by: Ahmed Allam <ahmed39652003@gmail.com>

strix/config/models.py

@@ -59,6 +59,42 @@ DEFAULT_MODEL_RETRY = ModelRetrySettings(
     ),
 )
 
+RECOMMENDED_MODEL_NAMES = (
+    "openai/gpt-5.6",
+    "openai/gpt-5.6-sol",
+    "openai/gpt-5.6-terra",
+    "openai/gpt-5.5",
+    "openai/gpt-5.5-pro",
+    "openai/gpt-5.4",
+    "openai/gpt-5.3-codex",
+    "anthropic/claude-fable-5",
+    "anthropic/claude-opus-4-8",
+    "anthropic/claude-opus-4-7",
+    "anthropic/claude-sonnet-5",
+    "anthropic/claude-sonnet-4-6",
+    "vertex_ai/gemini-3.1-pro-preview",
+    "gemini/gemini-3.1-pro-preview",
+    "deepseek/deepseek-v4-pro",
+    "deepseek/deepseek-v4-flash",
+    "dashscope/qwen3.7-max-2026-06-08",
+    "moonshot/kimi-k2.7-code",
+    "moonshot/kimi-k2.6",
+)
+
+_RECOMMENDED_MODEL_NAME_SET = frozenset(name.lower() for name in RECOMMENDED_MODEL_NAMES)
+
+FRONTIER_MODEL_FAMILIES = (
+    (("azure", "azure_ai", "bedrock_mantle", "openai"), ("gpt-5",)),
+    (
+        ("anthropic", "azure_ai", "bedrock", "claude", "databricks", "snowflake", "vertex_ai"),
+        ("claude-fable-5", "claude-opus-4", "claude-sonnet-5", "claude-sonnet-4"),
+    ),
+    (("google", "gemini", "vertex_ai"), ("gemini-3",)),
+    (("deepseek",), ("deepseek-v4", "deepseek-r1", "deepseek-reasoner")),
+    (("alibaba", "dashscope", "qwen"), ("qwen3.7", "qwen3.5", "qwen3-max")),
+    (("moonshot", "moonshotai", "kimi"), ("kimi-k2.7", "kimi-k2.6", "kimi-k2.5")),
+)
+
 
 def configure_sdk_model_defaults(settings: Settings) -> None:
     """Apply Strix config to SDK-native defaults."""
@@ -180,6 +216,78 @@ def model_supports_reasoning(model_name: str) -> bool:
     return bool(entry and entry.get("supports_reasoning"))
 
 
+def is_recommended_or_frontier_model(model_name: str) -> bool:
+    """Return whether a model is recommended or in a frontier model family."""
+    name = _normalized_model_name(model_name)
+    if not name:
+        return False
+    if name in _RECOMMENDED_MODEL_NAME_SET:
+        return True
+    provider_name, bare_model_name = _split_model_provider(name)
+    return any(
+        _matches_frontier_family(provider_name, bare_model_name, provider_markers, prefixes)
+        for provider_markers, prefixes in FRONTIER_MODEL_FAMILIES
+    )
+
+
+def _normalized_model_name(model_name: str) -> str:
+    name = model_name.strip().lower()
+    for prefix in ("litellm/", "any-llm/"):
+        if name.startswith(prefix):
+            name = name[len(prefix) :]
+            break
+    return name
+
+
+def _split_model_provider(model_name: str) -> tuple[str | None, str]:
+    if "/" not in model_name:
+        return None, model_name
+    provider_name, bare_model_name = model_name.rsplit("/", 1)
+    return provider_name, bare_model_name
+
+
+def _matches_frontier_family(
+    provider_name: str | None,
+    model_name: str,
+    provider_markers: tuple[str, ...],
+    model_prefixes: tuple[str, ...],
+) -> bool:
+    if not _matches_model_prefix(model_name, model_prefixes):
+        return False
+    if provider_name is None:
+        return True
+    return _contains_provider_marker(
+        provider_name, provider_markers, split_compound_names=True
+    ) or _contains_provider_marker(model_name, provider_markers)
+
+
+def _matches_model_prefix(model_name: str, model_prefixes: tuple[str, ...]) -> bool:
+    return any(
+        candidate.startswith(prefix)
+        for candidate in _model_name_candidates(model_name)
+        for prefix in model_prefixes
+    )
+
+
+def _model_name_candidates(model_name: str) -> tuple[str, ...]:
+    if "." not in model_name:
+        return (model_name,)
+    suffixes = tuple(
+        model_name.split(".", index)[-1] for index in range(1, model_name.count(".") + 1)
+    )
+    return (model_name, *suffixes)
+
+
+def _contains_provider_marker(
+    value: str, provider_markers: tuple[str, ...], *, split_compound_names: bool = False
+) -> bool:
+    parts = set(value.replace(".", "/").split("/"))
+    if split_compound_names:
+        for separator in ("_", "-"):
+            parts.update(piece for part in tuple(parts) for piece in part.split(separator))
+    return any(marker in parts for marker in provider_markers)
+
+
 def is_known_openai_bare_model(model_name: str) -> bool:
     import litellm
 

strix/core/hooks.py

@@ -28,7 +28,10 @@ class ReportUsageHooks(RunHooks[dict[str, Any]]):
 
     def __init__(self, *, model: str, max_budget_usd: float | None = None) -> None:
         import math
-        if max_budget_usd is not None and (not math.isfinite(max_budget_usd) or max_budget_usd <= 0):
+
+        if max_budget_usd is not None and (
+            not math.isfinite(max_budget_usd) or max_budget_usd <= 0
+        ):
             raise ValueError("max_budget_usd must be a finite number greater than 0")
         self._model = model
         self._max_budget_usd = max_budget_usd

strix/interface/main.py

@@ -23,9 +23,11 @@ from strix.config import (
     persist_current,
 )
 from strix.config.models import (
+    RECOMMENDED_MODEL_NAMES,
     StrixProvider,
     configure_sdk_model_defaults,
     is_known_openai_bare_model,
+    is_recommended_or_frontier_model,
 )
 from strix.core.paths import run_dir_for, runtime_state_dir
 from strix.interface.cli import run_cli
@@ -264,7 +266,7 @@ def _provider_import_hint(exc: BaseException, model: str) -> str | None:
     return None
 
 
-async def warm_up_llm() -> None:
+async def warm_up_llm(show_model_warning: bool = True) -> None:
     console = Console()
     logger.info("Warming up LLM connection")
 
@@ -306,6 +308,32 @@ async def warm_up_llm() -> None:
             )
             sys.exit(1)
 
+        if show_model_warning and raw_model and not is_recommended_or_frontier_model(raw_model):
+            warn_text = Text()
+            warn_text.append("MODEL QUALITY WARNING", style="bold yellow")
+            warn_text.append("\n\n", style="white")
+            warn_text.append(f"'{raw_model}'", style="bold cyan")
+            warn_text.append(
+                " is not a recommended frontier model for Strix.\nSecurity scans work best with:\n",
+                style="white",
+            )
+            for recommended_model in RECOMMENDED_MODEL_NAMES:
+                warn_text.append(f"• {recommended_model}\n", style="bold cyan")
+            warn_text.append(
+                "\nYou can continue, but weaker models may miss vulnerabilities "
+                "or produce lower-quality findings.",
+                style="white",
+            )
+            console.print(
+                Panel(
+                    warn_text,
+                    title="[bold white]STRIX",
+                    title_align="left",
+                    border_style="yellow",
+                    padding=(1, 2),
+                ),
+            )
+
         model = StrixProvider().get_model(raw_model)
         await asyncio.wait_for(
             model.get_response(
@@ -827,7 +855,7 @@ def main() -> None:
     pull_docker_image()
 
     validate_environment()
-    asyncio.run(warm_up_llm())
+    asyncio.run(warm_up_llm(show_model_warning=args.non_interactive))
 
     persist_current()
 

strix/interface/tui/app.py

@@ -31,6 +31,7 @@ from textual.widgets import Button, Label, Static, TextArea, Tree
 from textual.widgets.tree import TreeNode
 
 from strix.config import load_settings
+from strix.config.models import is_recommended_or_frontier_model
 from strix.core.hooks import BudgetExceededError
 from strix.core.runner import run_strix_scan
 from strix.interface.tui.live_view import TuiLiveView
@@ -116,9 +117,16 @@ class SplashScreen(Static):  # type: ignore[misc]
         self._animation_timer: Timer | None = None
         self._panel_static: Static | None = None
         self._version = "dev"
+        self._non_frontier_model: str | None = None
 
     def compose(self) -> ComposeResult:
         self._version = get_package_version()
+        try:
+            model = (load_settings().llm.model or "").strip()
+        except Exception:
+            model = ""
+        if model and not is_recommended_or_frontier_model(model):
+            self._non_frontier_model = model
         self._animation_step = 0
         start_line = self._build_start_line_text(self._animation_step)
         panel = self._build_panel(start_line)
@@ -145,7 +153,7 @@ class SplashScreen(Static):  # type: ignore[misc]
         self._panel_static.update(panel)
 
     def _build_panel(self, start_line: Text) -> Panel:
-        content = Group(
+        rows = [
             Align.center(Text(self.BANNER.strip("\n"), style=self.PRIMARY_GREEN, justify="center")),
             Align.center(Text(" ")),
             Align.center(self._build_welcome_text()),
@@ -155,9 +163,26 @@ class SplashScreen(Static):  # type: ignore[misc]
             Align.center(start_line.copy()),
             Align.center(Text(" ")),
             Align.center(self._build_url_text()),
-        )
+        ]
+        if self._non_frontier_model:
+            rows.extend(
+                (
+                    Align.center(Text(" ")),
+                    Align.center(self._build_model_warning_text(self._non_frontier_model)),
+                )
+            )
 
-        return Panel.fit(content, border_style=self.PRIMARY_GREEN, padding=(1, 6))
+        return Panel.fit(Group(*rows), border_style=self.PRIMARY_GREEN, padding=(1, 6))
+
+    @staticmethod
+    def _build_model_warning_text(model: str) -> Text:
+        text = Text("⚠ ", style=Style(color="yellow", bold=True))
+        text.append(model, style=Style(color="cyan", bold=True))
+        text.append(
+            " is not a recommended frontier model - pentest quality could be degraded",
+            style=Style(color="yellow"),
+        )
+        return text
 
     def _build_url_text(self) -> Text:
         return Text("strix.ai", style=Style(color=self.PRIMARY_GREEN, bold=True))

strix/runtime/docker_client.py

@@ -49,7 +49,7 @@ logger = logging.getLogger(__name__)
 class StrixDockerSandboxClient(DockerSandboxClient):
     # Host directories to bind-mount into the container, set by the docker
     # backend before ``create()``. Each item is ``{source, target, read_only}``.
-    strix_bind_mounts: list[dict[str, Any]] = []  # overridden per-instance in backends.py
+    strix_bind_mounts: list[dict[str, Any]] | None = None
 
     async def _create_container(
         self,

tests/test_models.py

@@ -0,0 +1,66 @@
+"""Tests for LLM model recommendation helpers."""
+
+from __future__ import annotations
+
+import pytest
+
+from strix.config.models import RECOMMENDED_MODEL_NAMES, is_recommended_or_frontier_model
+
+
+@pytest.mark.parametrize("model_name", RECOMMENDED_MODEL_NAMES)
+def test_recommended_models_are_accepted(model_name: str) -> None:
+    assert is_recommended_or_frontier_model(model_name)
+
+
+def test_recommended_models_are_matched_case_insensitively() -> None:
+    assert is_recommended_or_frontier_model("Vertex_AI/Gemini-3-Pro-Preview")
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "gpt-5.5",
+        "litellm/openai/gpt-5.4-pro",
+        "azure_ai/gpt-5.5-pro",
+        "bedrock_mantle/openai.gpt-5.5",
+        "anthropic/claude-opus-4-8",
+        "anthropic.claude-opus-4-8",
+        "anthropic/claude-opus-4-7",
+        "anthropic/claude-fable-5",
+        "anthropic/claude-sonnet-5",
+        "vertex_ai/claude-sonnet-5@default",
+        "vertex_ai/claude-sonnet-4-6@default",
+        "any-llm/anthropic/claude-sonnet-4-6",
+        "vertex_ai/gemini-3.1-pro-preview",
+        "openrouter/google/gemini-3.1-pro-preview",
+        "deepseek/deepseek-v4-pro",
+        "deepseek/deepseek-r1-0528",
+        "deepseek/deepseek-reasoner",
+        "dashscope/qwen3-max-2026-01-23",
+        "qwen3.7-max",
+        "moonshot/kimi-k2.6",
+        "kimi-k2.7-code",
+    ],
+)
+def test_frontier_model_families_are_accepted(model_name: str) -> None:
+    assert is_recommended_or_frontier_model(model_name)
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "",
+        "openai/gpt-4.1",
+        "anthropic/claude-3-5-sonnet-latest",
+        "ollama/llama3.1",
+        "deepseek/deepseek-chat",
+        "custom-ollama/gpt-5-mini-local",
+        "custom-provider/claude-opus-4-local",
+        "xai/grok-4.5",
+        "openrouter/x-ai/grok-4",
+        "mistral/mistral-medium-3-5",
+        "mistral/magistral-medium-latest",
+    ],
+)
+def test_non_frontier_models_are_rejected(model_name: str) -> None:
+    assert not is_recommended_or_frontier_model(model_name)