Currently, when a security analyst discovers a vulnerability in critical software system, they must navigate a fraught dilemma: immediately disclosing the vulnerability to the public could harm the system’s users; whereas disclosing the vulnerability only to the software’s vendor lets the vendor disregard or deprioritize the security risk, to the detriment of unwittingly-affected users. A compelling recent line of work aims to resolve this by using Zero Knowledge (ZK) protocols that let analysts prove that they know a vulnerability in a program, without revealing the details of the vulnerability or the inputs that exploit it. In principle, this could be achieved by generic ZK techniques. In practice, ZK vulnerability proofs to date have been restricted in scope and expressibility, due to challenges related to generating proof statements that model real-world software at scale and to directly formulating violated properties. This article presents Cheesecloth , a novel proof-statement compiler, which proves practical vulnerabilities in ZK by soundly-but-aggressively preprocessing programs on public inputs, selectively revealing information about executed control segments, and formalizing information leakage using a novel storage-labeling scheme. Cheesecloth ’s practicality is demonstrated by generating ZK proofs of well-known vulnerabilities in (previous versions of) critical software, including the Heartbleed information leakage in OpenSSL, a memory vulnerability in the FFmpeg multimedia encoding framework, a cryptographic implementation bug in the Secure Scuttlebutt decentralised social network, and a denial of service vulnerability in OpenSSL.

Verifying systems by implementing them in the programming language of a proof assistant (e.g., Gallina for Coq) lets us directly leverage the full power of the proof assistant for verifying the system. But, to execute such an implementation requires extraction, a large complicated process that is in the trusted computing base (TCB).

Rust is a modern systems language that provides guaranteed memory safety through static analysis. However, Rust includes an escape hatch in the form of "unsafe code," which the compiler assumes to be memory safe and to preserve crucial pointer aliasing invariants. Unsafe code appears in many data structure implementations and other essential libraries, and bugs in this code can lead to memory safety violations in parts of the program that the compiler otherwise proved safe. We present CRUST, a tool combining exhaustive test generation and bounded model checking to detect memory safety errors, as well as violations of Rust's pointer aliasing invariants within unsafe library code. CRUST requires no programmer annotations, only an indication of the modules to check. We evaluate CRUSTon data structures from the Rust standard library. It detects memory safety bugs that arose during the library's development and remained undetected for several months.

Current app stores distribute some malware to unsuspecting users, even though the app approval process may be costly and time-consuming. High-integrity app stores must provide stronger guarantees that their apps are not malicious. We propose a verification model for use in such app stores to guarantee that the apps are free of malicious information flows. In our model, the software vendor and the app store auditor collaborate -- each does tasks that are easy for her/him, reducing overall verification cost. The software vendor provides a behavioral specification of information flow (at a finer granularity than used by current app stores) and source code annotated with information-flow type qualifiers. A flow-sensitive, context-sensitive information-flow type system checks the information flow type qualifiers in the source code and proves that only information flows in the specification can occur at run time. The app store auditor uses the vendor-provided source code to manually verify declassifications.