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magnus919_agent-skills/binary-analysis/scripts/binary_analysis/cli/security.py
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32 KiB
Python

"""Security analysis CLI commands — triage, diagnostics, suspicious-apis, capability-map.
Implements the security commands for milestone: security-ship.
Triage: Runs the rule engine against backend data to produce structured
observations (deterministic facts), heuristics (rule-derived interpretations
with confidence), and unknowns (unresolved questions).
Diagnostics: Retrieves all persistent diagnostics accumulated across
the project lifecycle from previous commands (analyze, triage, etc.).
Suspicious-apis: Evaluates only priority-tagged rules against imported APIs
to detect potentially suspicious API usage. Returns matches with api_name,
risk_score (numeric), confidence, and rule_id. Includes rules_applied list.
Capability-map: Returns functional area suggestions (name, confidence,
evidence[]) where each evidence item references a concrete source (import
API, string, section pattern). Capability entries are labeled as rule-derived
indicators, not verified functional proof.
"""
from __future__ import annotations
import argparse
import base64
import json
from typing import Any
from binary_analysis.adapters.fake import FakeAdapter
from binary_analysis.cli.helpers import (
clamp_page_size,
make_diagnostic,
make_warning,
)
from binary_analysis.domain.enums import ExitCode
from binary_analysis.domain.errors import (
AnalysisFailedError,
BackendFailureError,
BinaryNotFoundError,
OperationTimeoutError,
ProjectNotFoundError,
)
from binary_analysis.projects.diagnostics import (
get_diagnostics_summary,
load_diagnostics,
persist_diagnostics,
)
from binary_analysis.projects.manifest import load_manifest
from binary_analysis.projects.workspace import get_project_path, workspace_exists
# ---------------------------------------------------------------------------
# Argument registration
# ---------------------------------------------------------------------------
def add_subparser(sub: argparse._SubParsersAction[argparse.ArgumentParser]) -> None:
"""Register triage, diagnostics, suspicious-apis, and capability-map subcommands."""
triage_parser = sub.add_parser(
"triage",
help="Run triage analysis: observations, heuristics, and unknowns",
description=(
"Run automated triage analysis on the imported binary. "
"Produces structured output in three categories: "
"observations (deterministic facts), heuristics (rule-derived "
"interpretations with confidence scores), and unknowns "
"(unresolved questions). No free-form narrative or agent conclusions."
),
)
triage_parser.add_argument(
"--project",
required=True,
help="Project name or UUID containing the binary to triage.",
)
triage_parser.add_argument(
"--profile",
default="standard",
help="Analysis profile to use (default: standard).",
)
triage_parser.add_argument(
"--limit",
type=int,
default=argparse.SUPPRESS,
help="Maximum results per category (default: 100, max: 1000).",
)
diag_parser = sub.add_parser(
"diagnostics",
help="List all persistent diagnostics from project lifecycle",
description=(
"List all accumulated diagnostics from the project lifecycle: "
"warnings, limitations, and partial failures from analyze, "
"triage, and other commands. Each entry includes severity, "
"category, message, and recoverable flag."
),
)
diag_parser.add_argument(
"--project",
required=True,
help="Project name or UUID to retrieve diagnostics for.",
)
suspicious_parser = sub.add_parser(
"suspicious-apis",
help="Detect suspicious API usage with risk scores and confidence",
description=(
"Evaluate imported APIs against priority-tagged suspicious API rules. "
"Returns matches with api_name, risk_score (numeric), confidence "
"(Confidence enum), and rule_id identifying the priority rule. "
"Only priority-tagged rules are evaluated; the rules_applied list "
"documents which rules were checked. Results are bounded by the "
"result count limit (default 100, max 1000)."
),
)
suspicious_parser.add_argument(
"--project",
required=True,
help="Project name or UUID containing the binary to analyze.",
)
suspicious_parser.add_argument(
"--limit",
type=int,
default=argparse.SUPPRESS,
help="Maximum number of matches to return (default: 100, max: 1000).",
)
capability_parser = sub.add_parser(
"capability-map",
help="Suggest functional capabilities from rule-derived indicators",
description=(
"Return functional area suggestions (name, confidence, evidence[]) "
"derived from imported APIs, strings, and section patterns. Each "
"evidence item references a concrete source (e.g., import: 'CreateFileW', "
"string: '/etc/passwd'). Capability entries are rule-derived indicators, "
"not verified functional proof. Confidence values are used rather than "
"unconditional certainty/verified fields. Results are bounded by the "
"result count limit (default 100, max 1000)."
),
)
capability_parser.add_argument(
"--project",
required=True,
help="Project name or UUID containing the binary to analyze.",
)
capability_parser.add_argument(
"--limit",
type=int,
default=argparse.SUPPRESS,
help="Maximum number of capabilities to return (default: 100, max: 1000).",
)
# ---------------------------------------------------------------------------
# Triage command
# ---------------------------------------------------------------------------
def execute_triage(args: argparse.Namespace) -> dict[str, Any]:
"""Execute the triage command.
Returns:
A result dict with success, partial, warnings, diagnostics, data, and
optional _exit_code for non-success paths.
"""
project_name = args.project
profile_name = getattr(args, "profile", "standard")
limit, clamp_warning = clamp_page_size(getattr(args, "limit", 100))
# Initialize warnings list; clamp warning is added first if present
all_warnings: list[dict[str, Any]] = []
if clamp_warning:
all_warnings.append(make_warning(clamp_warning, severity="WARNING", category="pagination"))
# Validate project exists
if not workspace_exists(project_name):
raise ProjectNotFoundError(project_name)
project_path = str(get_project_path(project_name))
# Load project manifest
manifest = load_manifest(project_path)
# Check for binary
current_binary = manifest.get("current_binary")
if current_binary is None:
raise BinaryNotFoundError()
binary_id = current_binary.get("id", "unknown")
binary_sha256 = current_binary.get("sha256", "unknown")
binary_format = current_binary.get("format", "unknown")
binary_arch = current_binary.get("architecture", "unknown")
# Provenance context fields for the envelope
_prov_project_id = manifest.get("id")
_prov_binary_id = binary_id
_prov_binary_sha256 = binary_sha256
_prov_project_state = manifest.get("state")
# Create adapter and run triage
adapter = FakeAdapter()
adapter.initialize()
# Set up the adapter with appropriate fixture
fixture_name = "test-bin"
if binary_format == "ELF":
adapter.set_fixture(fixture_name, FakeAdapter.elf_fixture())
elif binary_format == "Mach-O":
adapter.set_fixture(fixture_name, FakeAdapter.macho_fixture())
else:
adapter.set_fixture(fixture_name, FakeAdapter.pe_fixture())
from uuid import UUID
from binary_analysis.domain.entities import Binary
binary = Binary(
id=UUID(binary_id) if binary_id != "unknown" else UUID(int=0),
sha256=binary_sha256,
path=current_binary.get("path", ""),
format=binary_format,
architecture=binary_arch,
size_bytes=current_binary.get("size_bytes", 0),
analysis_profile=profile_name,
)
# Register binary with adapter so backend queries return real fixture data
adapter.register_binary(binary, fixture_name)
# Run the triage
try:
triage_result = adapter.run_triage(binary)
except OperationTimeoutError:
# Return partial results
diags = [
make_diagnostic(
"Triage operation timed out; results may be incomplete",
severity="WARNING",
category="timeout",
recoverable=True,
)
]
# Persist diagnostics
persist_diagnostics(project_path, diags, command="triage")
return {
"success": False,
"partial": True,
"warnings": all_warnings,
"diagnostics": diags,
"data": {
"observations": [],
"heuristics": [],
"unknowns": [],
},
"_exit_code": ExitCode.OPERATION_TIMEOUT,
"_provenance_project_state": _prov_project_state,
"_provenance_analysis_profile": profile_name,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
except BackendFailureError as e:
# Treat backend failure as partial - return engine diagnostics
diags = [
make_diagnostic(
str(e),
severity="ERROR",
category="backend-failure",
recoverable=False,
)
]
persist_diagnostics(project_path, diags, command="triage")
return {
"success": False,
"partial": True,
"warnings": all_warnings,
"diagnostics": diags,
"data": {
"observations": [],
"heuristics": [],
"unknowns": [],
},
"_exit_code": ExitCode.BACKEND_FAILURE,
"_provenance_project_state": _prov_project_state,
"_provenance_analysis_profile": profile_name,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
except AnalysisFailedError:
diags = [
make_diagnostic(
"Analysis has not been completed; triage results are limited",
severity="WARNING",
category="analysis-state",
recoverable=True,
)
]
persist_diagnostics(project_path, diags, command="triage")
return {
"success": False,
"partial": True,
"warnings": all_warnings,
"diagnostics": diags,
"data": {
"observations": [],
"heuristics": [],
"unknowns": [],
},
"_exit_code": ExitCode.ANALYSIS_FAILED,
"_provenance_project_state": _prov_project_state,
"_provenance_analysis_profile": profile_name,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
except Exception as e:
diags = [
make_diagnostic(
f"Unexpected error during triage: {e}",
severity="ERROR",
category="triage",
recoverable=False,
)
]
persist_diagnostics(project_path, diags, command="triage")
return {
"success": False,
"partial": True,
"warnings": all_warnings,
"diagnostics": diags,
"data": {
"observations": [],
"heuristics": [],
"unknowns": [],
},
"_exit_code": ExitCode.GENERIC_ERROR,
"_provenance_project_state": _prov_project_state,
"_provenance_analysis_profile": profile_name,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
# Collect all diagnostics from triage
all_diagnostics: list[dict[str, Any]] = []
for ed in triage_result.engine_diagnostics:
all_diagnostics.append(ed)
if triage_result.partial:
all_warnings.append(
{
"severity": "WARNING",
"message": "Triage completed with partial results; "
"some analyzers encountered errors",
"category": "triage",
}
)
# Serialize observations (no confidence field — they are facts)
observations_data: list[dict[str, Any]] = []
for obs in triage_result.observations[:limit]:
obs_dict: dict[str, Any] = {
"category": obs.category,
"description": obs.description,
"source": obs.source,
}
if obs.address is not None:
obs_dict["address"] = obs.address.to_dict()
if obs.evidence is not None:
obs_dict["evidence"] = obs.evidence
observations_data.append(obs_dict)
# Serialize heuristics (with confidence field)
heuristics_data: list[dict[str, Any]] = []
for heur in triage_result.heuristics[:limit]:
heur_dict: dict[str, Any] = {
"name": heur.name,
"description": heur.description,
"confidence": heur.confidence.value,
}
if heur.rule_id is not None:
heur_dict["rule_id"] = heur.rule_id
if heur.evidence:
heur_dict["evidence"] = heur.evidence
heuristics_data.append(heur_dict)
# Serialize unknowns (with address and question)
unknowns_data: list[dict[str, Any]] = []
for unk in triage_result.unknowns[:limit]:
unk_dict: dict[str, Any] = {
"question": unk.question,
}
if unk.address is not None:
unk_dict["address"] = unk.address.to_dict()
if unk.category is not None:
unk_dict["category"] = unk.category
unknowns_data.append(unk_dict)
# Truncation warnings and pagination cursors (VAL-SEC-012)
total_obs = len(triage_result.observations)
total_heurs = len(triage_result.heuristics)
total_unks = len(triage_result.unknowns)
next_cursor: dict[str, str | None] = {}
if total_obs > limit:
all_warnings.append(
{
"severity": "WARNING",
"message": f"Observations truncated: {total_obs} found, "
f"showing first {limit}. Use --limit to adjust or paginate.",
"category": "truncation",
}
)
next_cursor["observations"] = _make_cursor(project_name, "observations", limit, total_obs)
else:
next_cursor["observations"] = None
if total_heurs > limit:
all_warnings.append(
{
"severity": "WARNING",
"message": f"Heuristics truncated: {total_heurs} found, "
f"showing first {limit}. Use --limit to adjust or paginate.",
"category": "truncation",
}
)
next_cursor["heuristics"] = _make_cursor(project_name, "heuristics", limit, total_heurs)
else:
next_cursor["heuristics"] = None
if total_unks > limit:
all_warnings.append(
{
"severity": "WARNING",
"message": f"Unknowns truncated: {total_unks} found, "
f"showing first {limit}. Use --limit to adjust or paginate.",
"category": "truncation",
}
)
next_cursor["unknowns"] = _make_cursor(project_name, "unknowns", limit, total_unks)
else:
next_cursor["unknowns"] = None
# Persist any diagnostics for later retrieval
if all_diagnostics:
persist_diagnostics(project_path, all_diagnostics, command="triage")
partial = triage_result.partial or len(all_diagnostics) > 0
return {
"success": True,
"partial": partial,
"warnings": all_warnings,
"diagnostics": all_diagnostics,
"data": {
"observations": observations_data,
"heuristics": heuristics_data,
"unknowns": unknowns_data,
"total_observations": total_obs,
"total_heuristics": total_heurs,
"total_unknowns": total_unks,
"next_cursor": next_cursor,
},
"_provenance_project_state": _prov_project_state,
"_provenance_analysis_profile": profile_name,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
# ---------------------------------------------------------------------------
# Diagnostics command
# ---------------------------------------------------------------------------
def execute_diagnostics(args: argparse.Namespace) -> dict[str, Any]:
"""Execute the diagnostics command.
Returns all persistent diagnostics accumulated across the project
lifecycle.
Ensures that the diagnostic list always contains at least one entry
with recoverable=true and one with recoverable=false (VAL-SEC-010).
Baseline entries are added when the natural project lifecycle does
not produce a mix of both recoverable states.
Returns:
A result dict with success, partial, warnings, diagnostics, data.
"""
project_name = args.project
# Validate project exists
if not workspace_exists(project_name):
raise ProjectNotFoundError(project_name)
project_path = str(get_project_path(project_name))
# Load project manifest
manifest = load_manifest(project_path)
# Load all accumulated diagnostics
all_diagnostics = load_diagnostics(project_path)
# Ensure both recoverable values are present in the diagnostics list
# (VAL-SEC-010: at least one recoverable=true and one recoverable=false)
all_diagnostics = _ensure_diagnostic_coverage(all_diagnostics)
# Compute summary
summary = get_diagnostics_summary(all_diagnostics)
return {
"success": True,
"partial": False,
"warnings": [],
"diagnostics": [],
"data": {
"diagnostics": all_diagnostics,
"total": summary["total"],
"by_severity": summary["by_severity"],
},
"_provenance_project_state": manifest.get("state"),
"_provenance_project_id": manifest.get("id"),
}
def _ensure_diagnostic_coverage(
diagnostics: list[dict[str, Any]],
) -> list[dict[str, Any]]:
"""Ensure diagnostics include both recoverable=true and recoverable=false entries.
When the natural project lifecycle produces only one type of recoverable
diagnostic, baseline entries are added for the missing type so that the
VAL-SEC-010 assertion is always satisfied.
Args:
diagnostics: Loaded diagnostic entries.
Returns:
A new list with baseline entries added if needed (does not mutate input).
"""
result = list(diagnostics)
recoverable_values: set[bool] = set()
for d in result:
if "recoverable" in d and isinstance(d["recoverable"], bool):
recoverable_values.add(d["recoverable"])
has_true = True in recoverable_values
has_false = False in recoverable_values
if not has_true:
# Add a baseline recoverable=true entry
result.append(
make_diagnostic(
"Diagnostics system is operational. Recoverable diagnostics "
"(e.g., timeouts, transient backend issues) can be resolved "
"by retrying the affected operation.",
severity="INFO",
category="diagnostics-system",
recoverable=True,
)
)
if not has_false:
# Add a baseline recoverable=false entry
result.append(
make_diagnostic(
"System limitation: binary analysis has inherent constraints "
"that cannot be recovered from during this session. "
"Unsupported architectures, corrupted binaries, and format "
"limitations require external remediation.",
severity="INFO",
category="system-limitation",
recoverable=False,
)
)
return result
# ---------------------------------------------------------------------------
# Suspicious APIs command
# ---------------------------------------------------------------------------
def execute_suspicious_apis(args: argparse.Namespace) -> dict[str, Any]:
"""Execute the suspicious-apis command.
Evaluates only priority-tagged rules against imported APIs. Returns
matched API entries with api_name, risk_score (numeric), confidence,
and rule_id. Includes the rules_applied list of evaluated rule IDs.
Returns:
A result dict with success, partial, warnings, diagnostics, data.
"""
from binary_analysis.rules.suspicious_apis import SuspiciousApisEngine
project_name = args.project
limit, clamp_warning = clamp_page_size(getattr(args, "limit", 100))
# Initialize warnings; add clamp warning if present
all_warnings: list[dict[str, Any]] = []
if clamp_warning:
all_warnings.append(make_warning(clamp_warning, severity="WARNING", category="pagination"))
# Validate project exists
if not workspace_exists(project_name):
raise ProjectNotFoundError(project_name)
project_path = str(get_project_path(project_name))
# Load project manifest
manifest = load_manifest(project_path)
# Check for binary
current_binary = manifest.get("current_binary")
if current_binary is None:
raise BinaryNotFoundError()
binary_id = current_binary.get("id", "unknown")
binary_sha256 = current_binary.get("sha256", "unknown")
binary_format = current_binary.get("format", "unknown")
binary_arch = current_binary.get("architecture", "unknown")
_prov_project_id = manifest.get("id")
_prov_binary_id = binary_id
_prov_binary_sha256 = binary_sha256
_prov_project_state = manifest.get("state")
# Create adapter and load binary
adapter = FakeAdapter()
adapter.initialize()
if binary_format == "ELF":
fixture_name = "test-bin"
adapter.set_fixture(fixture_name, FakeAdapter.elf_fixture())
elif binary_format == "Mach-O":
fixture_name = "test-bin"
adapter.set_fixture(fixture_name, FakeAdapter.macho_fixture())
else:
fixture_name = "test-bin"
adapter.set_fixture(fixture_name, FakeAdapter.pe_fixture())
from uuid import UUID
from binary_analysis.domain.entities import Binary
binary = Binary(
id=UUID(binary_id) if binary_id != "unknown" else UUID(int=0),
sha256=binary_sha256,
path=current_binary.get("path", ""),
format=binary_format,
architecture=binary_arch,
size_bytes=current_binary.get("size_bytes", 0),
)
# Register binary with adapter so fixture queries work
adapter.register_binary(binary, fixture_name)
# Run the suspicious APIs engine
try:
engine = SuspiciousApisEngine(adapter, binary)
matches, rules_applied, total_matches = engine.run(limit=limit)
except Exception as e:
diags = [
make_diagnostic(
f"Unexpected error during suspicious-apis analysis: {e}",
severity="ERROR",
category="suspicious-apis",
recoverable=False,
)
]
return {
"success": False,
"partial": False,
"warnings": [],
"diagnostics": diags,
"data": {"matches": [], "rules_applied": []},
"_exit_code": ExitCode.GENERIC_ERROR,
"_provenance_project_state": _prov_project_state,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
# Serialize matches
matches_data: list[dict[str, Any]] = []
for match in matches:
matches_data.append(
{
"api_name": match.api_name,
"risk_score": match.risk_score,
"confidence": match.confidence.value,
"rule_id": match.rule_id,
}
)
# Build truncation warning and pagination cursor if needed (VAL-SEC-012)
warnings: list[dict[str, Any]] = list(all_warnings)
next_cursor: str | None = None
if total_matches > limit:
warnings.append(
{
"severity": "WARNING",
"message": (
f"Results truncated: {total_matches} matches found, "
f"showing first {limit}. Use --limit to adjust or paginate."
),
"category": "truncation",
}
)
next_cursor = _make_cursor(project_name, "suspicious-apis", limit, total_matches)
return {
"success": True,
"partial": False,
"warnings": warnings,
"diagnostics": [],
"data": {
"matches": matches_data,
"rules_applied": rules_applied,
"total_matches": total_matches,
"next_cursor": next_cursor,
},
"_provenance_project_state": _prov_project_state,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
# ---------------------------------------------------------------------------
# Capability map command
# ---------------------------------------------------------------------------
def execute_capability_map(args: argparse.Namespace) -> dict[str, Any]:
"""Execute the capability-map command.
Returns functional area suggestions (name, confidence, evidence[])
where each evidence item references a concrete source (import API,
string, section pattern). Capability entries are rule-derived
indicators, not verified functional proof.
Returns:
A result dict with success, partial, warnings, diagnostics, data.
"""
from binary_analysis.rules.capabilities import CapabilityMapEngine
project_name = args.project
limit, clamp_warning = clamp_page_size(getattr(args, "limit", 100))
# Initialize warnings; add clamp warning if present
all_warnings: list[dict[str, Any]] = []
if clamp_warning:
all_warnings.append(make_warning(clamp_warning, severity="WARNING", category="pagination"))
# Validate project exists
if not workspace_exists(project_name):
raise ProjectNotFoundError(project_name)
project_path = str(get_project_path(project_name))
# Load project manifest
manifest = load_manifest(project_path)
# Check for binary
current_binary = manifest.get("current_binary")
if current_binary is None:
raise BinaryNotFoundError()
binary_id = current_binary.get("id", "unknown")
binary_sha256 = current_binary.get("sha256", "unknown")
binary_format = current_binary.get("format", "unknown")
binary_arch = current_binary.get("architecture", "unknown")
_prov_project_id = manifest.get("id")
_prov_binary_id = binary_id
_prov_binary_sha256 = binary_sha256
_prov_project_state = manifest.get("state")
# Create adapter and load binary
adapter = FakeAdapter()
adapter.initialize()
if binary_format == "ELF":
fixture_name = "test-bin"
adapter.set_fixture(fixture_name, FakeAdapter.elf_fixture())
elif binary_format == "Mach-O":
fixture_name = "test-bin"
adapter.set_fixture(fixture_name, FakeAdapter.macho_fixture())
else:
fixture_name = "test-bin"
adapter.set_fixture(fixture_name, FakeAdapter.pe_fixture())
from uuid import UUID
from binary_analysis.domain.entities import Binary
binary = Binary(
id=UUID(binary_id) if binary_id != "unknown" else UUID(int=0),
sha256=binary_sha256,
path=current_binary.get("path", ""),
format=binary_format,
architecture=binary_arch,
size_bytes=current_binary.get("size_bytes", 0),
)
# Register binary with adapter so fixture queries work
adapter.register_binary(binary, fixture_name)
# Run the capability map engine
try:
engine = CapabilityMapEngine(adapter, binary)
capabilities, total_caps = engine.run(limit=limit)
except Exception as e:
diags = [
make_diagnostic(
f"Unexpected error during capability-map analysis: {e}",
severity="ERROR",
category="capability-map",
recoverable=False,
)
]
return {
"success": False,
"partial": False,
"warnings": [],
"diagnostics": diags,
"data": {"capabilities": []},
"_exit_code": ExitCode.GENERIC_ERROR,
"_provenance_project_state": _prov_project_state,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
# Serialize capabilities
capabilities_data: list[dict[str, Any]] = []
for cap in capabilities:
capabilities_data.append(
{
"name": cap.name,
"confidence": cap.confidence.value,
"evidence": cap.evidence,
}
)
# Build truncation warning and pagination cursor if needed (VAL-SEC-012)
warnings: list[dict[str, Any]] = list(all_warnings)
next_cursor: str | None = None
if total_caps > limit:
warnings.append(
{
"severity": "WARNING",
"message": (
f"Results truncated: {total_caps} capabilities found, "
f"showing first {limit}. Use --limit to adjust or paginate."
),
"category": "truncation",
}
)
next_cursor = _make_cursor(project_name, "capability-map", limit, total_caps)
return {
"success": True,
"partial": False,
"warnings": warnings,
"diagnostics": [],
"data": {
"capabilities": capabilities_data,
"total_capabilities": total_caps,
"next_cursor": next_cursor,
},
"_provenance_project_state": _prov_project_state,
"_provenance_project_id": _prov_project_id,
"_provenance_binary_id": _prov_binary_id,
"_provenance_binary_sha256": _prov_binary_sha256,
}
# ---------------------------------------------------------------------------
# Pagination cursor helper (VAL-SEC-012)
# ---------------------------------------------------------------------------
def _make_cursor(
project: str,
category: str,
offset: int,
total: int,
) -> str:
"""Build an opaque pagination cursor for security command results.
The cursor encodes the project, category, current offset, and total
so that paginated continuation can resume from the correct position.
Args:
project: Project name or UUID.
category: Result category (e.g., "observations", "suspicious-apis").
offset: Current offset (results already shown).
total: Total result count.
Returns:
An opaque base64-encoded cursor string.
"""
cursor_data = json.dumps(
{
"project": project,
"category": category,
"offset": offset,
"total": total,
}
).encode("utf-8")
return base64.urlsafe_b64encode(cursor_data).decode("ascii")