25,124 research outputs found
CapablePtrs: Securely Compiling Partial Programs using the Pointers-as-Capabilities Principle
Capability machines such as CHERI provide memory capabilities that can be
used by compilers to provide security benefits for compiled code (e.g., memory
safety). The C to CHERI compiler, for example, achieves memory safety by
following a principle called "pointers as capabilities" (PAC). Informally, PAC
says that a compiler should represent a source language pointer as a machine
code capability. But the security properties of PAC compilers are not yet well
understood. We show that memory safety is only one aspect, and that PAC
compilers can provide significant additional security guarantees for partial
programs: the compiler can provide guarantees for a compilation unit, even if
that compilation unit is later linked to attacker-controlled machine code. This
paper is the first to study the security of PAC compilers for partial programs
formally. We prove for a model of such a compiler that it is fully abstract.
The proof uses a novel proof technique (dubbed TrICL, read trickle), which is
of broad interest because it reuses and extends the compiler correctness
relation in a natural way, as we demonstrate. We implement our compiler on top
of the CHERI platform and show that it can compile legacy C code with minimal
code changes. We provide performance benchmarks that show how performance
overhead is proportional to the number of cross-compilation-unit function
calls
Fine-grained Language Composition: A Case Study
Although run-time language composition is common, it normally takes the form
of a crude Foreign Function Interface (FFI). While useful, such compositions
tend to be coarse-grained and slow. In this paper we introduce a novel
fine-grained syntactic composition of PHP and Python which allows users to
embed each language inside the other, including referencing variables across
languages. This composition raises novel design and implementation challenges.
We show that good solutions can be found to the design challenges; and that the
resulting implementation imposes an acceptable performance overhead of, at
most, 2.6x.Comment: 27 pages, 4 tables, 5 figure
Parametric Inference of Memory Requirements for Garbage Collected Languages
The accurate prediction of program's memory requirements is a critical component in software development. Existing heap space analyses either do not take deallocation into account or adopt specific models of garbage collectors which do not necessarily correspond to the actual memory usage. We present a novel approach to inferring upper bounds on memory requirements of Java-like programs which is parametric on the notion of object lifetime, i.e., on when objects become collectible. If objects lifetimes are inferred by a reachability analysis, then our analysis infers accurate upper bounds on the memory consumption for a reachability-based garbage collector. Interestingly, if objects lifetimes are inferred by a heap liveness analysis, then we approximate the program minimal memory requirement, i.e., the peak memory usage when using an optimal garbage collector which frees objects as soon as they become dead. The key idea is to integrate information on objects lifetimes into the process of generating the recurrence equations which capture the memory usage at the different program states. If the heap size limit is set to the memory requirement inferred by our analysis, it is ensured that execution will not exceed the memory limit with the only assumption that garbage collection works when the limit is reached. Experiments on Java bytecode programs provide evidence of the feasibility and accuracy of our analysis
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