fix(ingest): inspect unverified OMS bundle contents

README.md

@@ -934,13 +934,15 @@ SkillSpector uses a two-stage detection pipeline:
 - High recall (catches most issues)
 - Moderate precision (some false positives)
 
-A valid, root-level OpenSSF Model Signing signature (`skill.oms.sig`) is retained in the
-component inventory as type `oms_signature`, but excluded from static and LLM content analysis.
-OMS bundles necessarily contain long base64-encoded payload, signature, and certificate fields;
-generic obfuscated-code checks can otherwise misclassify those fields as hidden executable content.
-The recognizer checks the minimal OMS DSSE/in-toto structure; it does not verify the signature,
-certificate chain, transparency-log entry, or signer identity. Invalid or unrecognized signature
-files are scanned normally.
+OpenSSF Model Signing / DSSE bundles remain in content analysis, regardless of
+filename. Static and LLM analyzers inspect decoded JSON payloads, readable DER
+certificate/signature content, and all unknown wrapper fields. Strictly parsed
+binary fields are projected rather than treated as executable base64; canonical
+bytes remain available to YARA and fingerprints. Unsupported encodings (including
+environments without the optional `cryptography` dependency) retain raw fields
+and produce an explicit incomplete-analysis warning. Findings in decoded content
+point to the original bundle and label their projection in evidence. None of this
+verifies signatures, certificate chains, transparency logs, or signer identity.
 
 ### Stage 2: LLM Semantic Analysis (Optional)
 - Evaluates context and intent
@@ -969,7 +971,7 @@ The tool requires outbound HTTPS access to `api.osv.dev` for live vulnerability
 SkillSpector is defense-in-depth, not a sandbox. Know what it does and does not do before relying on it:
 
 - **It never executes the scanned skill.** All analysis is static (regex, Python AST, YARA) plus optional LLM evaluation of file *contents* — the skill's code is never run.
-- **LLM analysis sends analyzer-eligible file contents to the configured provider.** When LLM analysis is enabled (the default), file contents are sent to the active `SKILLSPECTOR_PROVIDER` endpoint. Recognized OMS signature files are excluded. Use `--no-llm` to keep contents local (static analysis only).
+- **LLM analysis sends analyzer-eligible file contents to the configured provider.** When LLM analysis is enabled (the default), file contents are sent to the active `SKILLSPECTOR_PROVIDER` endpoint. DSSE payloads and readable signing metadata are included. Use `--no-llm` to keep contents local (static analysis only).
 - **SC4 sends dependency names to OSV.dev.** The supply-chain check queries [OSV.dev](https://osv.dev) with the package names and versions the skill declares, to look up known CVEs. This is fundamental to the check and runs even with `--no-llm`. It sends dependency coordinates (not file contents), requires no API key, and falls back to a bundled list when OSV.dev is unreachable.
 - **It does not sandbox the host.** SkillSpector flags risky patterns *before* you install a skill; it does not contain or isolate a skill you choose to install anyway.
 

docs/DEVELOPMENT.md

@@ -81,7 +81,7 @@ All targets assume the virtual environment is **already created and activated**.
 | `zip_bytes`, `mode` | Optional zip input and scan mode |
 | `components` | List of relative file paths in the skill |
 | `file_cache` | Map of path → file contents |
-| `inspection_ledger` | Structured evidence for files excluded, skipped, or failed during analysis; a recognized OMS signature is recorded as an `oms_signature` scope exclusion. |
+| `inspection_ledger` | Structured evidence for files excluded, skipped, or failed during analysis; unsupported DSSE decoding is recorded as an `oms_signature` partial event; successfully projected bundles remain in analysis without a scope exclusion. |
 | `ast_cache` | Map of path → AST representation (for future use) |
 | `manifest`, `previous_manifest` | Parsed skill metadata (e.g. from SKILL.md) |
 | `component_metadata` | List of dicts: path, type, lines, executable, size_bytes (from build_context) |

src/skillspector/inspection_ledger.py

@@ -156,7 +156,7 @@ class LedgerReason(StrEnum):
     LedgerReason.MANIFEST_ABSENT: ("No compatible manifest was present for this analyzer."),
     LedgerReason.NO_APPLICABLE_FILES: ("No files matched this analyzer's applicability contract."),
     LedgerReason.OMS_SIGNATURE: (
-        "Recognized OMS signature metadata is excluded from content analysis."
+        "DSSE content decoding is incomplete or unsupported; raw fields were retained for inspection. Signature authenticity was not verified."
     ),
     LedgerReason.BASELINE_FILE: (
         "The explicitly selected suppression baseline is excluded from content analysis."

src/skillspector/nodes/analyzers/static_runner.py

@@ -57,6 +57,7 @@
     observe_analyzer_findings,
 )
 from skillspector.nodes.deduplicate import classification_metadata_key
+from skillspector.oms import project_oms_content
 from skillspector.python_ast import (
     MAX_PYTHON_AST_SOURCE_CHARS,
     ParsedPythonFile,
@@ -2310,9 +2311,46 @@ def _scan_all_views_detailed(
     started_at: float | None = None,
     python_ast: ParsedPythonFile | None = None,
     python_source: bool | None = None,
+    _oms_projected: bool = False,
 ) -> tuple[list[Finding], LedgerReason | None, dict[str, int | float]]:
     """Scan bounded raw windows and return any limit with observed/limit metrics."""
     started_at = time.monotonic() if started_at is None else started_at
+    runtime_limit = MAX_STATIC_ANALYSIS_SECONDS_PER_ARTIFACT
+    if timeout_seconds is not None:
+        runtime_limit = min(runtime_limit, max(0.0, timeout_seconds))
+    now = time.monotonic()
+    if now >= started_at + runtime_limit:
+        return (
+            [],
+            LedgerReason.RUNTIME_LIMIT,
+            {
+                "observed_seconds": max(0.0, now - started_at),
+                "limit_seconds": runtime_limit,
+            },
+        )
+    projection = None if _oms_projected else project_oms_content(content)
+    if projection is not None and projection.view.name != "raw":
+        findings, reason, metrics = _scan_all_views_detailed(
+            path,
+            projection.view.text,
+            pattern_modules,
+            None,
+            max_findings=max_findings,
+            timeout_seconds=timeout_seconds,
+            started_at=started_at,
+            # This view is derived JSON metadata, not a Python execution surface.
+            python_source=False,
+            _oms_projected=True,
+        )
+        for finding in findings:
+            finding.start_line = finding.end_line = 1
+            finding.start_column = finding.end_column = None
+            finding.evidence["oms_projection"] = (
+                "Decoded content in the original DSSE bundle; line 1 identifies the carrier."
+            )
+            finding.evidence[_SOURCE_START_EVIDENCE] = projection.view.source_offset(0)
+            finding.evidence.pop(_SOURCE_END_EVIDENCE, None)
+        return findings, reason, metrics
     ast_modules = [module for module in pattern_modules if _uses_python_ast(module)]
     lexical_modules = [module for module in pattern_modules if not _uses_python_ast(module)]
     if python_source is None:

src/skillspector/nodes/build_context.py

@@ -76,6 +76,7 @@
     is_executable_content,
     is_zip_content,
 )
+from skillspector.oms import project_oms_content
 from skillspector.python_ast import (
     PythonSourceClassification,
     classify_python_source,
@@ -908,7 +909,7 @@ def _decode_base64_json(value: object) -> dict[str, object] | None:
 
 
 def _is_valid_oms_signature_bytes(data: bytes) -> bool:
-    """Recognize the minimal OMS DSSE/in-toto structure from bounded bytes."""
+    """Recognize OMS structure only; this does not verify or trust its contents."""
     try:
         if len(data) > MAX_FILE_BYTES:
             return False
@@ -2897,16 +2898,7 @@ def _record_processing_runtime(*, phase: str, path: str, now: float) -> None:
             elif signature_valid:
                 recognized_oms_signature_paths.add(_OMS_SIGNATURE_PATH)
     recognized_oms_signatures = frozenset(recognized_oms_signature_paths)
-    signature_events = [
-        ledger_event(
-            outcome=LedgerOutcome.OUT_OF_SCOPE,
-            record_type=LedgerRecordType.SCOPE_BOUNDARY,
-            phase="discovery",
-            path=path,
-            reason=LedgerReason.OMS_SIGNATURE,
-        )
-        for path in sorted(recognized_oms_signatures)
-    ]
+    signature_events: list[InspectionLedgerEvent] = []
     baseline_events = [
         ledger_event(
             outcome=LedgerOutcome.OUT_OF_SCOPE,
@@ -2917,12 +2909,6 @@ def _record_processing_runtime(*, phase: str, path: str, now: float) -> None:
         )
         for path in sorted(selected_baselines)
     ]
-    for artifact in artifact_inventory:
-        if artifact["path"] in recognized_oms_signatures:
-            artifact["disposition"] = ArtifactDisposition.OUT_OF_SCOPE
-            artifact["reason"] = LedgerReason.OMS_SIGNATURE.value
-            llm_file_cache.pop(artifact["path"], None)
-
     primary_path = next(
         (path for path in ("SKILL.md", "skill.md") if path in inventoried_components), None
     )
@@ -3090,8 +3076,6 @@ def _record_processing_runtime(*, phase: str, path: str, now: float) -> None:
     ordinary_components: list[str] = []
     for path in cache_candidates:
         ordinary_artifact = inventory_by_path.get(path)
-        if path in recognized_oms_signatures:
-            continue
         if path in raw_file_cache or (
             ordinary_artifact is not None
             and ordinary_artifact.get("disposition")
@@ -3474,7 +3458,7 @@ def mark_excluded_nested_metadata(
             ]
         )
     )
-    for path in [*recognized_containers, *recognized_oms_signatures]:
+    for path in recognized_containers:
         llm_file_cache.pop(path, None)
 
     postprocessing_events: list[InspectionLedgerEvent] = []
@@ -3647,6 +3631,46 @@ def _mark_runtime_partial(affected_paths: list[str], first_limited_path: str) ->
             )
         )
 
+    for path, content in local_file_cache.items():
+        if not content.lstrip().startswith("{"):
+            continue
+        projection_started = monotonic()
+        if projection_started >= processing_deadline:
+            _record_processing_runtime(
+                phase="signature_projection", path=path, now=projection_started
+            )
+            continue
+        projection = project_oms_content(content)
+        if projection is None:
+            continue
+        projection_finished = monotonic()
+        if projection_finished >= processing_deadline:
+            _record_processing_runtime(
+                phase="signature_projection", path=path, now=projection_finished
+            )
+            continue
+        artifact = inventory_by_path.get(path)
+        bounded_provider_view = artifact is not None and (
+            artifact.get("disposition") == ArtifactDisposition.PARTIAL
+            and artifact.get("reason")
+            in {LedgerReason.SIZE_LIMIT.value, LedgerReason.TOTAL_BYTES_LIMIT.value}
+        )
+        if path in llm_file_cache and projection.view.name != "raw" and not bounded_provider_view:
+            llm_file_cache[path] = projection.view.text
+        if not projection.complete:
+            signature_events.append(
+                ledger_event(
+                    outcome=LedgerOutcome.PARTIAL,
+                    record_type=LedgerRecordType.SYSTEM,
+                    phase="discovery",
+                    path=path,
+                    reason=LedgerReason.OMS_SIGNATURE,
+                )
+            )
+            if artifact is not None and artifact["disposition"] == ArtifactDisposition.ANALYZED:
+                artifact["disposition"] = ArtifactDisposition.PARTIAL
+                artifact["reason"] = LedgerReason.OMS_SIGNATURE.value
+
     llm_components = sorted(llm_file_cache)
     file_cache = dict(llm_file_cache)
     manifest_events: list[InspectionLedgerEvent] = []