Memory Leaks are Memory Safe

By Huon Wilson — Published 04 Apr 2016

Contents

    Memory unsafety and memory leaks are arguably the two categories of bugs that have received the most attention for prevention and mitigation. As their names suggest, they are in the same part of “bug space”, however they are in some ways diametric opposites, and solving one does not solve the other. The widespread use of memory-safe managed languages hammers this point home: they avoid some memory unsafety by presenting a “leak everything” model to programmers.

    Put simply: memory unsafety is doing something with invalid data, a memory leak is not doing something with valid data. In table form:

      Valid data Invalid data
    Used 👍 Memory unsafety
    Not used Memory leak 👍

    The best programs lie in the 👍 cells only: they manipulate valid things, and don’t manipulate invalid ones. Passable programs might also have some valid data that they don’t use (leak memory), but bad ones will try to use invalid data.

    When a language, such as Rust, advertises itself as memory safe, it isn’t saying anything about whether memory leaks are impossible.

    Consequences

    The most important difference between memory unsafety and memory leaks in practice is the scope of their possible results, with one easily very serious, and the other usually just annoying.

    Memory safety is a key building block in any other form of safety/program correctness. If a program is not memory safe, there are very few guarantees about its behaviour, due to the possibility of memory corruption. A malicious party interacting with a memory unsafe program may be able to exploit the unsafety to read private keys straight out of a server’s memory or to execute arbitrary code on someone else’s computer.

    On the other hand, a memory leak will generally, at worst, lead to a denial-of-service, where a useful program is killed due to using too much memory (and, as it grows to this stage, the computer may be rendered essentially inoperable due to memory pressure). This also can be caused by a malicious attacker, but the damage is usually very much more controlled. Of course, a denial-of-service can be extremely annoying, and there are places where this is a critical problem, but memory unsafety would generally be equally problematic—more likely, more problematic. (Additionally, given memory unsafety’s inherent lack of control, a problem there could easily lead to a denial-of-service similar/identical to that which a memory leak can cause.)

    Given this, most programming languages choose to tolerate memory leaks (they allow data not be deallocated/cleaned up after the last time it is used), but not memory unsafety. That is, most “memory safe languages” guarantee all programs written in them have no unsafety0, and they only try—usually, try hard—to help programmers avoid leaks, but without making a hard promise.

    delete free

    There are a few different ways to get memory unsafety, but there’s one category (from the Wikipedia article) that stands out when we’re discussing memory management:

    • Dynamic memory errors - incorrect management of dynamic memory and pointers:
      • Dangling pointer - a pointer storing the address of an object that has been deleted.
      • Double free - repeated calls to free may prematurely free a new object at the same address. If the exact address has not been reused, other corruption may occur, especially in allocators that use free lists.
      • Invalid free - passing an invalid address to free can corrupt the heap.
      • Null pointer accesses will cause an exception or program termination in most environments, but can cause corruption in operating system kernels or systems without memory protection, or when use of the null pointer involves a large or negative offset.

    In that list, only null pointer accesses aren’t caused by deallocating memory—calling the free function to mark an allocation as unused/return it to the operating system—incorrectly. And thus, one way to be guaranteed to avoid three quarters of those possibilities is to just never call free: if memory is never released, it is impossible to suffer from the problems caused by releasing it. In terms of the table above, removing free is removing the “Invalid data” column: all data is always valid.

    Of course, just disallowing free has some downsides1, particularly making it very annoying to write programs that don’t eventually use all available memory. However, computers are infallible in ways humans are not, so maybe we could allow them to call free

    Optimising leaks

    A large fraction of modern code is written in languages designed to be memory safe, languages like Java, Javascript, Python and Ruby. They have no explicit free, and so automatically manage memory (hence “managed language”) via a garbage collector built into the runtime systems shipped with the languages’ compilers and interpreters.

    At its core2, garbage collection is a way to make it feasible to expose a programming model where all allocations leak. Letting a garbage collector manage every allocation theoretically allows programs (and programmers) to pretend that memory is infinite, not needing to carefully track when memory isn’t needed any more: programs do whatever they want, and the GC will automatically and dynamically free chunks of memory that are guaranteed to be unneeded, ensuring the program’s memory use remains under control. Almost all garbage collectors determine neededness conservatively by finding things no longer accessible from the main program (the garbage collector itself needs to keep track of/have access to all allocations).

    In practice, the programmer has to think about non-infinite memory and its consequences a little more often than never, but memory unsafety concerns are removed, as desired. High-performance code often has to chose particular coding patterns to work-around deficiencies with garbage collectors (such as object pools to avoid touching the GC in tight loops), and one can accidentally create chains of references that keep large trees of data unnecessarily alive.

    However, even in the face of practical concerns, the point stands: without free, there’s no scope for some types of memory unsafety.

    Less leaky abstractions

    Given my status, I’d be remiss to mention an alternative to the leak-everything managed paradigm: instead using a technique that crosses out the whole “Invalid data” column, one can be more precise and cross out only the “Memory unsafety” cell. The Rust programming language does this.

    Rust doesn’t have C-style manual memory management, but rather RAII/scope-based resource management similar to C++, allowing types to have destructors for automatic clean-up. It does not literally have a free function users must remember to call (removing most of the “manual”), but the drop function serves the role of explicit free, allowing one to explicitly cause the destructor to be run on a value, thus invalidating it. In contrast to both C and C++, the language prevents use of such data at compile time to avoid memory unsafety.

    However, a programming model that’s not “leak everything” doesn’t mean it is “leak nothing”: the revised table for Rust (and anything similar) still has its memory leak cell.

      Valid data Invalid data
    Used 👍 Impossible
    Not used Memory leak 👍

    I’m not including this section because I think it’s a great promotion of Rust (being allowed to have invalid data that one can’t use doesn’t exactly sound world-shaking3…), but because that is the hole which this article is filling. The similarity of the phrases “memory leak” and “memory safety” regularly tricks people who have read “Rust is memory safe” into thinking Rust is (just) preventing memory leaks, leading to legitimate doubts about what Rust offers instead of, say, modern C++ in the space of low-level systems languages. Rust disallows memory unsafety, but memory leaks are possible.

    std::mem::forget

    Finally, returning to the title, Rust has the forget function, which throws away a value without actually running the destructor while still marking it invalid as if freed normally, thus possibly leaking memory. For a long time, this was marked as unsafe, that is, Rust was implicitly including memory leaks as something the programmer must opt-in to, like the risk of memory unsafety. However, this was not correct in practice, as things like reference cycles and thread deadlock could cause memory to leak. Rust decided to make forget safe, focusing its guarantees on just preventing memory unsafety and instead making only best-effort attempts towards preventing memory leaks (like essentially all other languages, memory safe and otherwise).

    Not all is lost!

    Like modern C++, the efforts Rust makes are pretty good, with RAII/scope-based resource management (specifically destructors) being a powerful tool for managing memory and beyond (and beyonder), especially when combined with Rust’s move-by-default semantics. The point about not being a guarantee is that (a) it’s not trivial to make a useful formal definition of memory leak (at the very least, usefulness varies depending on the context), and (b) there are relatively rare edge-cases that seem to be impossible to statically prevent without non-trivial cost. The wash-up in Rust’s standard library is all values have to be memory safe to leak, but they can still consider being leaked incorrect. In other words, one may get unwanted behaviour if a value is leaked, but the consequences will be more far controlled than a segfault or memory corruption.

    Comments:
    1. More specifically, programming languages will guarantee that one can only get unsafety by explicitly opting in to it, in some form, such as via Python’s ctypes module, or Rust’s unsafe keyword.

    2. It also has some upsides beyond just less memory unsafety: if one is OK without free, it becomes much easier to write programs where the lifetime of data is unclear, which makes many concurrent algorithms easier to write. That said, there are schemes for writing such code when manual frees are required, such as hazard pointers and the simpler epoch-based memory reclamation.

    3. It’s worth noting that the detailed knowledge of memory layout required for a top-flight garbage collectors lends itself to other tricks, such as allocations usually being a cheap pointer bump with a generational GC, and the ability for a moving GC to shift data around, improving cache locality (especially useful given the generally pointer-heavy nature of most managed languages). However, these tricks are orthogonal to both memory safety and memory leaks.

    4. It’s pretty useful, in that it allows move semantics to work, but that’s an article for another time, perhaps.

    I'm Huon Wilson huon_w, a post-graduate student in computational statistics. I'm a volunteer on Rust's core team.

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