Code injection attacks continue to plague applications that incorporate user input into executable programs. For example, SQL injection vulnerabilities rank fourth among all bugs reported in CVE, yet all previously proposed methods for detecting SQL injection attacks suffer from false positives and false negatives.

This paper describes the design and implementation of DIGLOSSIA, a new tool that precisely and efficiently detects code injection attacks on server-side Web applications generating SQL and NoSQL queries. The main problems in detecting injected code are (1) recognizing code in the generated query, and (2) determining which parts of the query are tainted by user input. To recognize code, DIGLOSSIA relies on the precise definition due to Ray and Ligatti. To identify tainted characters, DIGLOSSIA dynamically maps all application-generated characters to shadow characters that do not occur in user input and computes shadow values for all input-dependent strings. Any original characters in a shadow value are thus exactly the taint from user input.

Our key technical innovation is dual parsing. To detect injected code in a generated query, DIGLOSSIA parses the query in tandem with its shadow and checks that (1) the two parse trees are syntactically isomorphic, and (2) all code in the shadow query is in shadow characters and, therefore, originated from the application itself, as opposed to user input.

We demonstrate that DIGLOSSIA accurately detects both SQL and NoSQL code injection attacks while avoiding the false positives and false negatives of prior methods. By recasting the problem of detecting injected code as a string propagation and parsing problem, we gain substantial improvements in efficiency and precision over prior work. Our approach does not require any changes to the databases, Web servers, or Web browsers, adds virtually un- noticeable performance overhead, and is deployable today.