Information flow and declassification analysis for legacy and untrusted programs

Standard access control mechanisms are often insufficient to enforce compliance of programs with security policies. For this reason, information flow analysis has become a topic of increasing interest. In such type of analysis, the main property to be checked is called non-interference, which basically states that the publicly observable behaviour of a program is entirely independent of its secret, secure input values. However, simple non-interference is too restrictive for specifying and enforcing in- formation flow policies in most programs. Exceptions to non-interference are provided using declassification policies. Several approaches for enforcing declassification have been proposed in the literature. In most of these approaches, the declassification policies are embedded in the program itself or heavily tied to the variables in the program being analyzed, thereby providing at best little separation between the code and the policy. Consequently, the previous approaches essentially require that the code be trusted, since to trust that the correct policy is being enforced, we need to trust the source code. In this thesis, we propose a novel framework for information flow analysis, with support to declassification policies, related to the source code being analyzed via its I/O channels. The framework supports many of the of declassification policies identified in the literature. Based on flow-based static analysis, it represents a first step towards a new approach that can be applied to untrusted and legacy source code to automatically verify that the analyzed program complies with the specified declassification policies. We present a framework in which expressions over input channel values that could be output by the program are compared to a set of declassification requirements. We build an implementation of such framework, which works by constructing a conservative approximation of the such expressions, and by determining whether all of them satisfy the declassification requirements stated in the policy. We introduce a representation of such expressions that resembles tree automata. We prove that if a program is considered safe according to our analysis then it satisfies a property we call Policy Controlled Release, which formalizes information-flow correctness according to our notion of declassification policy. We demonstrate, through examples, that our approach works for several interesting and useful declassification policies, including one involving declassification of the average of several confidential values. Finally, we extend the static analyzer to build a practical hybrid static-runtime enforcement mechanism, consisting of 3 steps: static analysis, preload checking, and runtime enforcement. We demonstrate how the hybrid mechanism is able to enforce real-world policies which are unable to be treated by standard approaches from industry. Also, we show how this goal is achieved by keeping the static analysis step system independent, and the runtime enforcement with minimum runtime overhead

Shai: Enforcing Data-Specific Policies with Near-Zero Runtime Overhead

Data retrieval systems such as online search engines and online social networks must comply with the privacy policies of personal and selectively shared data items, regulatory policies regarding data retention and censorship, and the provider's own policies regarding data use. Enforcing these policies is difficult and error-prone. Systematic techniques to enforce policies are either limited to type-based policies that apply uniformly to all data of the same type, or incur significant runtime overhead. This paper presents Shai, the first system that systematically enforces data-specific policies with near-zero overhead in the common case. Shai's key idea is to push as many policy checks as possible to an offline, ahead-of-time analysis phase, often relying on predicted values of runtime parameters such as the state of access control lists or connected users' attributes. Runtime interception is used sparingly, only to verify these predictions and to make any remaining policy checks. Our prototype implementation relies on efficient, modern OS primitives for sandboxing and isolation. We present the design of Shai and quantify its overheads on an experimental data indexing and search pipeline based on the popular search engine Apache Lucene

Comprehensive and Practical Policy Compliance in Data Retrieval Systems

Data retrieval systems such as online search engines and online social networks process many data items coming from different sources, each subject to its own data use policy. Ensuring compliance with these policies in a large and fast-evolving system presents a significant technical challenge since bugs, misconfigurations, or operator errors can cause (accidental) policy violations. To prevent such violations, researchers and practitioners develop policy compliance systems. Existing policy compliance systems, however, are either not comprehensive or not practical. To be comprehensive, a compliance system must be able to enforce users' policies regarding their personal privacy preferences, the service provider's own policies regarding data use such as auditing and personalization, and regulatory policies such as data retention and censorship. To be practical, a compliance system needs to meet stringent requirements: (1) runtime overhead must be low; (2) existing applications must run with few modifications; and (3) bugs, misconfigurations, or actions by unprivileged operators must not cause policy violations. In this thesis, we present the design and implementation of two comprehensive and practical compliance systems: Thoth and Shai. Thoth relies on pure runtime monitoring: it tracks data flows by intercepting processes' I/O, and then it checks the associated policies to allow only policy-compliant flows at runtime. Shai, on the other hand, combines offline analysis and light-weight runtime monitoring: it pushes as many policy checks as possible to an offline (flow) analysis by predicting the policies that data-handling processes will be subject to at runtime, and then it compiles those policies into a set of fine-grained I/O capabilities that can be enforced directly by the underlying operating system

CONFLLVM: A Compiler for Enforcing Data Confidentiality in Low-Level Code

We present an instrumenting compiler for enforcing data confidentiality in low-level applications (e.g. those written in C) in the presence of an active adversary. In our approach, the programmer marks secret data by writing lightweight annotations on top-level definitions in the source code. The compiler then uses a static flow analysis coupled with efficient runtime instrumentation, a custom memory layout, and custom control-flow integrity checks to prevent data leaks even in the presence of low-level attacks. We have implemented our scheme as part of the LLVM compiler. We evaluate it on the SPEC micro-benchmarks for performance, and on larger, real-world applications (including OpenLDAP, which is around 300KLoC) for programmer overhead required to restructure the application when protecting the sensitive data such as passwords. We find that performance overheads introduced by our instrumentation are moderate (average 12% on SPEC), and the programmer effort to port OpenLDAP is only about 160 LoC.Comment: Technical report for CONFLLVM: A Compiler for Enforcing Data Confidentiality in Low-Level Code, appearing at EuroSys 201

JRIF: Reactive Information Flow Control for Java

A reactive information flow (RIF) automaton for a value v specifies (i) allowed uses for v and (ii) the RIF automaton for any value that might be directly or indirectly derived from v. RIF automata thus specify how transforming a value alters how the result might be used. Such labels are more expressive than existing approaches for controlling downgrading. We devised a type system around RIF automata and incorporated it into Jif, a dialect of Java that supports a classic form of labels for information flow. By implementing a compiler for the resulting JRIF language, we demonstrate how easy it is to replace a classic information-flow type system by a more expressive RIF-based type system. We programmed two example applications in JRIF, and we discuss insights they provide into the benefits of RIF-based security labels.Supported in part by AFOSR grants F9550-06-0019 and FA9550-11-1-0137, National Science Foundation grants 0430161, 0964409, and CCF-0424422 (TRUST), ONR grants N00014-01- 1-0968 and N00014-09-1-0652, and grants from Microsoft

Cryptographically Secure Information Flow Control on Key-Value Stores

We present Clio, an information flow control (IFC) system that transparently incorporates cryptography to enforce confidentiality and integrity policies on untrusted storage. Clio insulates developers from explicitly manipulating keys and cryptographic primitives by leveraging the policy language of the IFC system to automatically use the appropriate keys and correct cryptographic operations. We prove that Clio is secure with a novel proof technique that is based on a proof style from cryptography together with standard programming languages results. We present a prototype Clio implementation and a case study that demonstrates Clio's practicality.Comment: Full version of conference paper appearing in CCS 201