tainttracking

Tainttracking, often written as taint tracking, is a family of information flow analysis techniques designed to track how data from untrusted sources propagates through a program. The objective is to detect security vulnerabilities and privacy leaks by identifying cases where tainted input can influence sensitive operations without proper sanitization.

There are static taint analysis, dynamic taint tracking, and hybrid approaches. Static taint analysis examines source code or intermediate representations to infer taint flow without executing the program, offering broad coverage but facing challenges from aliasing, complex control flow, and path sensitivity. Dynamic taint tracking monitors a running program to propagate taint in real time, providing precise observations of actual execution paths but incurring runtime overhead. Hybrid approaches seek to combine these strengths.

Taint sources include user input, network data, environment variables, cookies, and other untrusted inputs. Sinks comprise database queries, system commands, file writes, HTTP headers, and authentication checks. Propagation typically follows assignments, arithmetic and string operations, and data structure manipulations; sanitization routines can clear taint or restrict its flow.

Implementation methods vary: runtime libraries that propagate taint, instrumentation of binaries or source code, and language-level frameworks that expose taint-tracking features. Notable examples include TaintDroid, a dynamic taint tracking system for Android, and Phosphor, a Java taint-tracking framework. Applications span web application security, mobile privacy protection, and data-leak prevention.

Limitations include performance overhead, possibility of false positives and negatives, and difficulties handling implicit information flows through control structures, concurrency, and complex data interactions. Tainttracking is commonly used with complementary security analyses to assess and mitigate risks.