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🛡️ Sentinel: [CRITICAL] Fix parameter default expressions taint leak #117

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🛡️ Sentinel: [CRITICAL] Fix parameter default expressions taint leak #117

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5 changes: 5 additions & 0 deletions .jules/sentinel.md
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Expand Up @@ -7,3 +7,8 @@
**Vulnerability:** The static analyzer was missing `yaml.unsafe_load` and `yaml.full_load` in its `_SERIALISATION_SINKS` mapping, potentially leading to false negatives when tracking untrusted data flowing into these dangerous deserialization functions.
**Learning:** Even if functions are listed in rule specifications (like `_SINK_SPECS`), they also need to be properly categorized in the core taint propagation logic (`_SERIALISATION_SINKS`) to ensure the analyzer correctly sheds validation provenance (converting output to `UNKNOWN_RAW`).
**Prevention:** When adding new sinks to rule definitions, always verify if they need to be added to core propagation mappings like `_SERIALISATION_SINKS` or `_PROPAGATING_BUILTINS`.

## 2026-06-21 - Evaluate Default Argument Expressions with Clean State
**Vulnerability:** Default parameter expressions in Python functions were being evaluated using the function's overall `function_taint` instead of a clean, baseline state.
**Learning:** For a taint analysis engine, parameter defaults must be evaluated independently of the current call context's `function_taint`. If an untrusted default expression uses `function_taint` (e.g. `ASSURED`), it can inadvertently launder untrusted data by marking it as safe simply because the enclosing function is trusted.
**Prevention:** In `_seed_parameters` within `src/wardline/scanner/taint/variable_level.py`, compute the taint of default arguments using `TaintState.INTEGRAL` rather than passing `function_taint` down as the base.
4 changes: 2 additions & 2 deletions src/wardline/scanner/taint/variable_level.py
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Expand Up @@ -767,12 +767,12 @@ def _seed_parameters(
param_idx = total_pos_args - num_defaults + i
if 0 <= param_idx < len(all_pos_params):
param_name = all_pos_params[param_idx].arg
default_taints[param_name] = _resolve_expr(default_expr, function_taint, taint_map or {}, {})
default_taints[param_name] = _resolve_expr(default_expr, TaintState.INTEGRAL, taint_map or {}, {})

@chatgpt-codex-connector chatgpt-codex-connector Bot Jul 19, 2026

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P1 Badge Preserve fail-closed taint for unknown default calls

When a default expression contains an unmodelled bare call, _resolve_call uses the supplied seed as its fallback; by passing INTEGRAL here, def f(x=unknown_provider()): return x now seeds x as clean instead of preserving the engine’s fail-closed unknown/raw fallback for unresolved calls. This can make L2 return summaries and downstream trusted callers treat data from unmodelled default providers as trusted, so the default evaluation needs a clean call-context without turning unresolved expressions into INTEGRAL.

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# Map keyword-only parameter defaults
for param, kw_default_expr in zip(args.kwonlyargs, args.kw_defaults, strict=True):
if kw_default_expr is not None:
default_taints[param.arg] = _resolve_expr(kw_default_expr, function_taint, taint_map or {}, {})
default_taints[param.arg] = _resolve_expr(kw_default_expr, TaintState.INTEGRAL, taint_map or {}, {})

def handle_arg(arg: ast.arg) -> None:
fallback = default_taints.get(arg.arg, function_taint)
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