Configuration-Aware Static Taint Analysis

• Configuration-aware static taint analysis is a technique that incorporates runtime configuration parameters into dataflow modeling to improve vulnerability detection and privacy leak diagnosis.
• It utilizes formal models, hybrid static/dynamic approaches, and machine learning to precisely track data dependencies across configuration-sensitive boundaries.
• Empirical studies show that these methods enhance diagnostic accuracy, reduce analysis time, and support regulatory compliance and robust security assessments.

Configuration-aware static taint analysis is an advanced methodology that systematically tracks data dependencies in a software system, taking into account runtime configuration parameters, conditional logic, and context-specific flows that can affect security and privacy outcomes. Unlike traditional static taint analysis, which often ignores configuration-sensitive branches or interface-specific logic, configuration-aware methods adapt their dataflow modeling to reflect real-world usage environments, system setups, and cross-component interactions. Such approaches have proven crucial for vulnerability detection, privacy leak diagnosis, regulatory compliance (e.g., GDPR), and enhanced diagnosability in highly configurable systems. The following sections present key principles, formal models, state-of-the-art frameworks, and empirical findings from core literature.

1. Formal Foundations and Dataflow Modeling

Configuration-aware static taint analysis builds upon foundational concepts of taint labeling and propagation, with explicit accommodation for runtime configuration parameters and context. In seminal work on privacy assessment, data flows in software architectures are abstractly modeled by annotating components or variables with taint labels indicating sensitivity (e.g., “tainted” or “untainted”) (Maltitz et al., 2016). The propagation semantics is formalized by a transfer function over a control-flow graph:

$T(v) = F(T(u), label(u, v))$

for every edge $(u, v)$ in the graph, where $T(\cdot)$ is the taint status and $F$ expresses the propagation rules, allowing the analysis to encode configuration-dependent flows.

A notable advancement is the integration of configuration metadata into the type graph, especially in API-centric models (such as GraphQL schemas), where tainted nodes are annotated with specific privilege and configuration requirements (Lambers et al., 15 Jan 2025). This enables the formalism to track not only data dependencies but also access control and policy constraints influenced by system configuration.

2. Configuration-Sensitive Taint Propagation

Systematic propagation of taint in the presence of configuration options or multi-component architectures requires enhanced static analysis algorithms. In Android analysis, IccTA demonstrates how inter-component data-flow across activities, services, and broadcast receivers is tracked by instrumenting the application code to maintain context (e.g., intents carrying config or sensitive data) (Li et al., 2014). The approach connects discontinuities in the control-flow graph by transforming component invocation points, enabling precise tracking across configuration-sensitive boundaries.

STELLA provides a pattern-driven extension for enclave code by focusing on interfaces (ECALL/OCALL) specified in configuration files (EDL) and applying propagation rules tailored to leaked pointer scenarios. Taint is tracked on value-flow graphs rather than plain control-flow, using a domain-specific set of rules capturing pointer and memory semantics:

• Load: $$T(\text{addr}), v = \text{load(addr)} \Rightarrow T(v)$$
• Store: $$T(v), \text{store}(v, \text{addr}) \Rightarrow T(\text{addr})$$
• GEP: $$T(\text{base}), \text{addr} = \text{gep(base)} \Rightarrow T(\text{addr})$$

This granularity ensures effective leak detection for pointer-based flows influenced by configuration between enclave and untrusted host (Chen et al., 2022).

3. Hybrid and Demand-Driven Techniques

To scale configuration-aware taint analysis to large, modular, or legacy systems, hybrid approaches and demand-driven algorithms have emerged as critical innovations. Hybrid frameworks combine static computation of critical data paths (“taint dependency sequences,” TDS) with dynamic search, often orchestrated by evolutionary algorithms to generate inputs that traverse configuration-specific paths to vulnerabilities (Rawat et al., 2013). The static component produces precise slices accounting for config branches; dynamic instrumentation records execution frequencies along these slices:

$F_i = \sum_{j=1}^{k} w_j \cdot f_{ij}$

where $w_j$ weights are dynamically tuned to configuration proximity and coverage.

Demand-driven IFDS-based analysis employs backward tracing from security-critical sinks, focusing only on paths relevant under current configuration, drastically reducing computational effort and improving scalability for industrial codebases (Allen et al., 2021).

4. Machine Learning and Probabilistic Context

Recent advances leverage statistical inference and machine learning to infer configuration-dependent taint models and improve precision. InspectJS integrates static analysis with learned inference for sink specifications by mining flow triples $(src, san, snk)$ in code corpora, using optimization over constraints of the form:

$$(n_{san}) + (n_{snk}) \leq \sum_{i=1}^{K} (n_{src_i}) + C + \epsilon$$

with code similarity metrics and UI-guided feedback to efficiently triage candidates (Dutta et al., 2021).

Bimodal taint analysis fuses static reasoning with neural modeling of developer conventions and documentation (natural language context). The framework flags unexpected flows—those not matching intended configuration usage—by classifying name and documentation context:

$$M : N \times (N_{fct} \times D) \to \{\text{Expected}, \text{Unexpected}\}$$

yielding high accuracy (F1 scores $\geq 0.85$) on major vulnerability types (Chow et al., 2023).

5. Automated Configuration Diagnosability and Logging

Configuration-aware static taint analysis is now applied to software diagnosability via enhanced logging. ConfLogger automates the identification of configuration-sensitive code segments by tracing configuration keys and their propagation using PDG-based taint analysis. This process is formally captured as:

$c_e = c_c \cup \{s_{log_1}, \dotsc, s_{log_q}\}$

where configuration-sensitive blocks $c_c$ are instrumented with diagnostic log statements $s_{log}$, including parameter identifiers, constraint checks, and actionable troubleshooting hints (Shan et al., 28 Aug 2025). Logs generated by LLMs (via chain-of-thought prompting) ensure coverage and direct exposure of configuration impact, yielding substantial gains in diagnostic precision and recall (F1 improvement of $26.2\%$ over prior tools).

6. Practical Impact and Empirical Results

Empirical evaluations consistently show that configuration-aware static taint analysis offers superior precision and recall in complex scenarios. For privacy leaks in Android, precision rates of $95.0\%$ and recall of $82.6\%$ have been attested in benchmarks (Li et al., 2014). In firmware analysis, integrating demand-driven aliasing and indirect call resolution revealed $192$ bugs, with $115$ CVEs assigned, and analysis times (mean: $3$ minutes/sample) far below prior methods (Cheng et al., 2021). In highly configurable systems, configuration-sensitive logging improves error localization rates to $100\%$ in silent failure scenarios and yields up to $1.25\times$ reductions in diagnostic time, as well as $251.4\%$ improvement in troubleshooting accuracy (Shan et al., 28 Aug 2025).

7. Methodological Extensions and Future Directions

Configuration-aware static taint analysis continues to evolve along several dimensions:

• Incorporating richer configuration metadata into formal models (e.g., token scopes, runtime roles in typed graphs) for API-centric security (Lambers et al., 15 Jan 2025).
• Applying automated inference of type qualifiers and polymorphic annotations to facilitate modular adoption in large Java codebases, with orders-of-magnitude runtime improvements (Karimipour et al., 25 Apr 2025).
• Leveraging question-driven debugging (TraceLens) to expose global impact of configuration modeling, enabling speculative “what-if” reasoning about source/sink/third-party library model changes (Yetiştiren et al., 10 Aug 2025).
• Extending automatic taint specification inference to sources and sanitizers; integrating abductive specification mining and dynamic techniques for full configuration coverage.

Configuration-aware taint analysis thus represents a convergence of advanced static analysis, formal modeling, hybrid runtime techniques, machine learning, and practical workflow integration, with proven effectiveness in complex, configurable software domains.