Beginning with the work of Forrest et al, several researchers have developed intrusion detection techniques based on modeling program behaviors in terms of system calls. A weakness of these techniques is that they focus on control flows involving system calls, but not their arguments. This weakness makes them susceptible to several classes of attacks, including attacks on security-critical data, race-condition and symbolic link attacks, and mimicry attacks. To address this weakness, we develop a new approach for learning dataflow behaviors of programs. The novelty in our approach, as compared to previous system-call argument learning techniques, is that it learns temporal properties involving the arguments of different system calls, thus capturing the flow of security-sensitive data through the program. An interesting aspect of our technique is that it can be uniformly layered on top of most existing control-flow models, and can leverage control-flow contexts to significantly increase the precision of dataflows captured by the model. This contrasts with previous system-call argument learning techniques that did not leverage control-flow information, and moreover, were focused on learning statistical properties of individual system call arguments. Through experiments, we show that temporal properties enable detection of many attacks that aren't detected by previous approaches. Moreover, they support formal reasoning about security assurances that can be provided when a program follows its dataflow behavior model, e.g., tar would read only files located within a directory specified as a command-line argument.

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This paper presents a new approach called model-carrying code (MCC) for safe execution of untrusted code. At the heart of MCC is the idea that untrusted code comes equipped with a concise high-level model of its security-relevant behavior. This model helps bridge the gap between high-level security policies and low-level binary code, thereby enabling analyses which would otherwise be impractical. For instance, users can use a fully automated verification procedure to determine if the code satisfies their security policies. Alternatively, an automated procedure can sift through a catalog of acceptable policies to identify one that is compatible with the model. Once a suitable policy is selected, MCC guarantees that the policy will not be violated by the code. Unlike previous approaches, the MCC framework enables code producers and consumers to collaborate in order to achieve safety. Moreover, it provides support for policy selection as well as enforcement. Finally, MCC makes no assumptions regarding the inherent risks associated with untrusted code. It simply provides the tools that enable a consumer to make informed decisions about the risk that he/she is willing to tolerate so as to benefit from the functionality offered by an untrusted application.