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End-to-End Mechanized Proof of an eBPF Virtual Machine for Micro-controllers

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13372)

Abstract

RIOT is a micro-kernel dedicated to IoT applications that adopts eBPF (extended Berkeley Packet Filters) to implement so-called femto-containers. As micro-controllers rarely feature hardware memory protection, the isolation of eBPF virtual machines (VM) is critical to ensure system integrity against potentially malicious programs. This paper shows how to directly derive, within the Coq proof assistant, the verified C implementation of an eBPF virtual machine from a Gallina specification. Leveraging the formal semantics of the CompCert C compiler, we obtain an end-to-end theorem stating that the C code of our VM inherits the safety and security properties of the Gallina specification. Our refinement methodology ensures that the isolation property of the specification holds in the verified C implementation. Preliminary experiments demonstrate satisfying performance.

Keywords

  • Mechanized proof
  • Virtual machines
  • Fault isolation

1 Introduction

figure a

Hardware-enforced memory isolation (e.g., Trustzone, Sanctum [6], Sancus [30]) is often not available on micro-controller units (MCU) which usually trade coarse-grain isolation for price and performance. To mitigate development variability and cost, common practices for MCU operating system design (RIOT [3], FreeRTOS, TinyOS, Fushia, and others [14]) advise to run all the device’s code stack in a shared memory space, which can only be reasonably safe if that code can be trusted. While standard in safety-critical system design, such a trust requirement is oftentimes unsuitable for networked MCUs, where the extensibility of the OS kernel at runtime is an essential functionality. When system reconfiguration does not affect the entire network (via, e.g., leader election), extensibility can easily be provided offline, by employing library OSs or unikernels [24], to reconfigure network endpoints independently (e.g., cloud apps). Otherwise, the best solution is to load and execute system extensions (configurations, protocols, firewalls, etc.) as assembly-level Wasm [13] or Berkeley Packet Filters [25] scripts using an interpreter or a Just-In-Time (JIT) compiler on the target device.

Femto-Containers. RIOT adopts the extended Berkeley Packet Filters (eBPF) and tailors it to resource-constrained MCUs by implementing so-called femto-containers: tiny virtual machine instances interpreting eBPF scripts. Compared to more expressive languages, like Wasm, experiments show that RIOT’s eBPF implementation, rBPF, requires less memory [39]. The Linux kernel features an eBPF JIT compiler whose security depends on a sophisticated online verifier [29]. As an MCU architecture cannot host such a large verifier, executing JIT code would imply delegation of trust to a third-party, offline, verifier. The alternative is to rely on a defensive VM. Though a VM may be slower than a JIT, it can run untrusted, erroneous, adversary code in an open, and possibly hostile environment, and still isolate faults to protect its host’s integrity.

Approach and Goals. This paper investigates an approach that trades high performance on low-power devices for defensive programming and low memory footprint. Our primary goal is to prevent faults that could compromise host devices and, by extension, force networked devices to reboot and resynchronize (i.e., fault tolerance protocols). To maximize trust in the implementation of rBPF, our refinement methodology allows the verified extraction of C code directly from its mechanically proved definition in Gallina, the functional language embedded in the Coq proof assistant [4].

Method. To mechanically prove the correctness of an interpreter, a conventional approach consists in defining the reference semantics in a proof assistant and in showing that an executable optimized interpreter produces the same output. In this paper, our goal is to verify the interpreter of the virtual rBPF instruction set, implemented with the system programming language C. To this aim, we introduce a direct, end-to-end, validation workflow. The semantics of the source instruction set is directly defined by monadic functional terms in our proof assistant. We prove that this semantics enforces safety and security requirements regarding memory isolation and control-flow integrity. Then, C code is automatically derived from these monadic functional terms to implement the expected virtual machine. We prove that the extracted C code has the same stateful behavior as the monadic specification. Our method uses a monadic subset of Gallina of sufficient expressiveness to specify rBPF’s semantics, supports the verified extraction of equivalent Clight [20] code, while provably implementing all required defensive runtime checks.

Plan. The rest of the paper is organized as follows. Section 2 states our contributions. Section 3 provides background on BPF and its variants, CompCert and the \(\partial x\) code extraction tool. Section 4 presents our workflow to formally refine monadic Gallina programs into C programs. Section 5 defines the proof model of our virtual machine: its semantics, consistency and isolation theorems. Section 6 refines the proof model of our femto-container into a synthesis model ready for code generation with CompCert. Section 7 proves the refinement between the synthesis and implementation models. Section 8 introduces our verified verifier which establishes the invariants needed by the VM. Section 9 case studies the performance of our generated VM implementation with respect to off-the-shelf RIOT femto-containers. Section 10 presents related works and Sect. 11 concludes.

2 Contributions

Implementing a fault-isolating virtual machine for MCUs faces two major challenges. One is to embed the VM inside the MCU’s micro-kernel and, hence, to minimize its code size and execution environment. A second challenge is to minimize the verification gap between its proof model and the running code. We address these challenges and present the first end-to-end verification and synthesis of a full-scale, real-world, virtual machine for the BPF instruction set family: CertrBPF, an interpreter tailored to the hardware and resources constraints of MCU architectures running the RIOT operating system. CertrBPF employs a workflow of proof-oriented programming using the functional language Gallina embedded in the proof assistant Coq. The verified refinement and extraction of an executable C program is performed directly from its proof model. We report the successful integration of CertrBPF into the open source IoT operating system RIOT and the evaluation of its performance against micro-benchmarks.

A Certified rBPF Interpreter. CertrBPF is a verified model and implementation of rBPF in Coq. We formalize the syntax and semantics of all rBPF instructions, implement a formal model of its interpreter (femto-container), complete the proof of critical properties of our model, and extract and verify CompCert C code from this formalization. This method allows us to obtain a fully verified virtual machine. Not only is the Gallina specification of the VM proved kernel- and memory-isolated using the proof assistant, but the direct interpretation of its intended semantics as CompCert C code is, itself, verified correct. This yields a fully verified binary program of maximum security and minimal memory footprint and reduced the Trusted Computing Base (TCB): CertrBPF, a memory-efficient kernel-level virtual machine that isolates runtime software faults using defensive code and does not necessitate offline verification.

End-to-End Proof Workflow. An obvious choice is to use the existing Coq extraction mechanism to compile the Gallina model into OCaml. The downside of this approach is that Coq extraction has to be trusted. Moreover the OCaml runtime needs to be trimmed down to fit space requirements of our target architecture and also becomes part of the TCB. Our ambition is instead to minimize the verification gap and provide an end-to-end security proof linking our Gallina model to, bare-metal, extracted C code. Our intended TCB is hence restricted to the Coq type-checker, the C semantics of the CompCert compiler and a pretty-printer for the generated C Abstract Syntax Tree (AST).

To reach this goal, our starting point is a model of the rBPF semantics written in Gallina. We use this proof model to certify that all the memory accesses are valid and isolated to dedicated memory areas, thus ensuring isolation. From this proof model, we then derive a synthesis model of which we extract an executable version in Clight, that we finally prove to perform the same state transitions.

Systems Integration and Micro-benchmarks. We integrate CertrBPF as a drop-in replacement of the current, non-verified, rBPF interpreter in the RIOT operating system. We then comparatively evaluate the performance of CertrBPF integrated in RIOT, running on various 32-bit micro-controller architectures. Our benchmarks demonstrate that, in practice, CertrBPF not just gains security, but reduces memory footprint as well as execution time.

3 Background

This section describes essential features of rBPF, of the CompCert compiler, and of the \(\partial x\) code generation tool, that are required by our refinement methodology.

BPF, eBPF and rBPF. Originally, the purpose of Berkeley Packet Filters [25] (BPF) was network packet filtering. The Linux community extended it to provide ways to run custom in-kernel VM code, hooked into various subsystems, for varieties of purposes beyond packet filtering [10]. eBPF was then ported to micro-controllers, yielding RIOT’s specification: rBPF [38]. Just as eBPF, rBPF is designed as a 64-bit register-based VM, using fixed-size 64-bit instructions and a reduced instruction set architecture. rBPF uses a fixed-size stack (512 bytes) and defines no heap interaction, which limits the VM memory overhead in RAM. The rBPF specification, however, does not define special registers or interrupts for flow control, nor support virtual memory: the host device’s memory is accessed directly and only guarded using permissions.

The CompCert Verified Compiler. CompCert [18] is a C compiler that is both programmed and proved correct using the Coq proof assistant. The compiler is structured into passes using several intermediate languages. Each intermediate language is equipped with a formal semantics and each pass is proved to preserve the observational behavior of programs.

The Clight Intermediate Language. Clight [20] is a pivotal language which condenses the essential features of C using a minimal syntax. The Verified Software Toolchain (VST) [2] verifies C programs at the Clight level that are obtained by the clightgen tool. Though we do not reuse the proof infrastructure of VST, we are reusing clightgen in order to get a Clight syntax from a C program.

CompCert Values and Memory Model  [19, 20]. The memory model and the representation of values are shared across all the intermediate languages of CompCert. The set of values val is defined as follows:

$$ val \ni v {::=} Vint (i) \mid Vlong (i) \mid Vptr (b,o) \mid Vundef \mid \dots $$

A value \(v\in val \) can be a 32-bit integer \( Vint (i)\); a 64-bit integer \( Vlong (i)\), a pointer \( Vptr (b,o)\) consisting of a block identifier b and an offset o, or the undefined value \( Vundef \). The undefined value \( Vundef \) represents an unspecified value and is not, strictly speaking, an undefined behavior. Yet, as most of the C operators are strict in \( Vundef \), and because branching over \( Vundef \) or de-referencing \( Vundef \) are undefined behaviors, our proofs will ensure the absence of \( Vundef \). CompCert values also include floating-point numbers; they play no role in the current development. CompCert’s memory consists of a collection of separate arrays. Each array has a fixed size determined at allocation time and is identified by an uninterpreted block \(b \in block \). The memory provides an API for loading values from memory and storing values in memory. Operations are parameterised by a memory chunk k which specifies how many bytes should be written or read and how to interpret bytes as a value \(v\in val \).

For instance, the memory chunk \( Mint32 \) specifies a 32-bit value and \( Mint64 \) a 64-bit value. The function \( load \ k\ m\ b\ o\) takes a memory chunk k, a memory m, a block b and an offset o. Upon success, it returns a value v obtained from the memory by reading bytes from the block b starting at index o. Similarly, the function \( store \ k\ m\ b\ o\ v\) takes a memory chunk k, a memory m, a block b, an offset o and a value v. Upon success, it returns an updated memory \(m'\) which is identical to m except that the block b contains the value v encoded into bytes according to the chunk k starting at offset o. The isolation properties offered by CompCert memory regions are worth mentioning: \( load \) and \( store \) operations fail (return None) for invalid offsets o and invalid permissions.

The \(\partial x\) tool. \(\partial x\) emerged from the toolchain used to design and verify the Pip proto-kernel [15]. Its aim was to allow writing most of Pip’s source code in Gallina in a style as close to C as possible. \(\partial x\) extracts C code from a Gallina source program in the form of a CompCert C AST. The goal of \(\partial x\) is to provide C programmers with readily reviewable code and thus avoid misunderstanding between those working on C/assembly modules (that access hardware) and those working on Coq modules (the code and proofs). To achieve this, \(\partial x\) handles a C-like subset of Gallina. The functions that are to be converted to C rely on a monad to represent the side effects of the computation, such as modifications to the CPU state. Yet \(\partial x\) does not mandate a particular monad for code extraction.

\(\partial x\) ’s Workflow. \(\partial x\) proceeds in two steps. First, given a list of Gallina functions, or whole modules, it generates an intermediate representation (IR) for the subset of Gallina it can handle. The second step is to translate this IR into a CompCert C AST. Since Coq has no built-in reflection mechanism, the first step is written in Elpi [8], using the Coq-Elpi plugin [37]. That step can also process external functions (appearing as extern in the extracted C code) to support separate compilation with CompCert. In order to obtain an actual C file, \(\partial x\) also provides a small OCaml function that binds the extracted C AST to CompCert’s C pretty-printer. Even though the \(\partial x\) language is a small subset of Gallina, it inherits much expressivity from the use of Coq types to manipulate values. For example, we can use bounded integers (i.e., the dependent pair of an integer with the proof that it is within some given range), that can be faithfully and efficiently represented as a single int in C. To this end, \(\partial x\) expects a configuration mapping Coq types to C.

\(\partial x\) Memory Management. A major design choice in the C-like subset of Gallina used by \(\partial x\) is memory management: its generated code executes without garbage collection. This affects the Coq types that can actually be used in \(\partial x\): recursive inductive types, such as lists, cannot automatically be converted. However, this Gallina subset is particularly relevant to programs in which one wants to precisely control memory management and decide how to represent data structures in memory. This is typically the case of an operating system or, in our case, the rBPF virtual machine.

4 A Workflow for End-to-End Verification in Coq

This section gives an overview of our methodology to derive a verified C implementation from a Gallina specification. In the following sections, the methodology will be instantiated to derive the C implementation of a fault-isolating rBPF virtual machine and its verifier. Our approach provides an end-to-end correctness proof, within the Coq proof assistant, that reduces the hurdle of reasoning directly over the C code.

As shown in Fig. 1, the original rBPF C implementation is first formalized by a proof model in Gallina, and the verification of expected properties (e.g., safety) is performed within the Coq proof assistant. This specification is then refined into an optimized (and equivalent) synthesis model ready for C-code extraction.

Fig. 1.
figure 1

End-to-end verification and synthesis workflow

The refinement and optimization principle employed by our method consists of deriving a C-ready implementation, in Gallina, that is as close as possible to the expected target C code. This principle allows to i) prove optimizations correct, ii) improve the performance of the extracted code and, iii) facilitate review and validation of extracted code with the system designers. From the C-ready Gallina implementation, we leverage \(\partial x\) to automatically generate C code and verify it: i) the generated C code is first parsed as a CompCert Clight model by the clightgen tool of VST and ii) it is proved to refine the source Gallina model in Coq using translation validation. Because \(\partial x\) generates C code in a syntax-directed manner, a minimal Clightlogic is designed to facilitate the refinement proof. The rest of the section explains these different steps in details.

Proof-Oriented Specification. Our specification takes the form of an executable abstract machine in monadic form. It uses the standard option-state monad M.

$$ \begin{array}{l} M\; a\; state := state \rightarrow {\textbf {option}} (a \times state) \\ returnM : a \rightarrow M \; a \; state := \lambda a.\lambda st. {\textbf {Some}} (a, st) \\ bindM : M \;a\; state \rightarrow (a \rightarrow M \; b\; state) \rightarrow M \; b\; state := \\ \qquad \qquad \lambda A.f. \lambda s. {\textbf {match}} \; A \; s\; {\textbf {with}} \; |\; {\textbf {None}} \Rightarrow {\textbf {None}} \; |\; {\textbf {Some}}(x, s') \Rightarrow (f \; x) \; s' \end{array} $$

In the remainder, we write \(\emptyset \) for None and \(\lfloor x \rfloor \) for \({\textbf {Some}}\; x\).

The monad threads the state along computations to model its in-place update. The safety property of the machine is implemented as an inline monitor: any violation leads to an unrecoverable error, i.e., the unique error represented by \(\emptyset \). One step of the machine has the following signature:

$$ step : M\ r\ state $$

where r is the type of the result. The \( step \) function implements a defensive semantics, checking the absence of error, dynamically. For our rBPF interpreter (see Sect. 5), the absence of error ensures that the rBPF code only performs valid instructions. In particular, all memory accesses are restricted to a sandbox specified as a list of memory regions. Function \( step \) is part of the TCB and, therefore, a mis-specification could result, after refinement, in an invalid computation. The purpose of the error state is to specify state transitions that would escape the scope of the safety property and, therefore, shall never be reachable from a well-formed state \( st \in wf \subseteq \mathcal {P}({state}) \). We require well-formedness to be an inductive property of the \( step \) function.

Theorem 1 (Well-formedness)

The \( step \) function preserves well-formedness.

$$ \forall st,st',r.\; st \in wf \wedge step \; st = \lfloor (r,st')\rfloor \Rightarrow st' \in wf $$

We also require that well-formedness is a sufficient condition to prevent the absence of error and, therefore, the safety of computations.

Theorem 2 (Safety)

The \( step \) function is safe, i.e., a well-formed state never leads to an error.

$$ \forall st.\; st \in wf \Rightarrow step \; st \ne \emptyset $$

C-Ready Implementation. Our methodology consists in refining the step function into an interpreter \(step_{\partial {x}}\) complying with the requirements of \(\partial x\). As \(\partial x\) performs syntax-directed code generation, the efficiency of the extracted code crucially depends on \(step_{\partial {x}}\). In order to preserve the absence of errors, we need a simulation relation between the step and \(step_{\partial {x}}\) functions. A direct consequence of the simulation thoerem is that \(step_{\partial {x}}\) never raises an error.

Theorem 3 (Simulation)

Given simulation relations \(Rs \subseteq state \times state'\) and \(Rr \subseteq r \times r'\), the function \(step_{\partial {x}}\) simulates the function step.

$$ \forall s_1, s_1', s_2,r. (s_1,s_2) \in Rs \wedge step \ s_1 = \lfloor r,s_1' \rfloor \Rightarrow \exists s_2', r'. \bigwedge \left\{ \begin{array}{l} step_{\partial {x}}\; s_2 = \lfloor r',s_2' \rfloor \\ (s_1',s_2') \in Rs \\ (r,r') \in Rr \end{array}\right. $$

Translation Validation of C Code. The next stage consists in refining the \(step_{\partial {x}}\) function into a Clight program by relying on \(\partial x\) to get a C program and on the clightgen tool to get a Clight \(step_C\) program (see Sect. 6). As this pass is not trusted, we require the following translation validation theorem.

Theorem 4 (Translation Validation)

Given a simulation relation \(Rs \subseteq state' \times val \times mem\) and a relation \(Rr \subseteq res \times val\), the Clight code \(step_C\) refines the function \(step_{\partial {x}}\):

$$ \begin{array}{l} \forall r, s, s', v, k, m. (s,v,m) \in Rs \Rightarrow step_{\partial {x}}\ s = \lfloor (r,s') \rfloor \Rightarrow \\ \quad \exists m',r'. Callstate(step_C,[v],k,m) {\rightarrow ^*}^t ReturnState(r',call\_cont(k),m') \wedge \\ \qquad (s',v,m') \in Rs \wedge (r,r') \in Rr \end{array} $$

Theorem 4 states that, if \(step_{\partial {x}}\; s\) runs without error and returns a result \((r,s')\), then, the Clight function \(step_C\) successfully runs with argument v and, after a finite number of execution steps, returns a result \(r'\) and a memory \(m'\) that preserve the refinement relations. In our encoding, the unique argument v is a pointer to the memory allocated region refining the interpreter state and k represents the continuation of the computation. A corollary of Theorem 4 is that the Clight code \(step_C\) is free of undefined behaviors. In particular, all memory accesses are valid. As the memory model does not allow to forge pointers, this yields a strong isolation property. In the remainder of this paper, for our rBPF virtual machine, we prove all the aforementioned properties within the Coq proof assistant.

5 A Proof-Oriented Virtual Machine Model

For our proof model, we define an explicit syntax for rBPF. We also define the state of the interpreter and semantic functions, in particular those implementing dynamic security checks. The rBPF instruction set, Fig. 2, features binary arithmetic and logic operations, negation, (un)conditional jumps relative to an offset, operations to load/store values from/to registers/memory, function calls, and termination. There are eleven 64-bit registers \(\{R0,\ldots , R10\}\); an immediate is 32-bit wide and an offset is 16-bit wide.

Fig. 2.
figure 2

Core syntax of rBPF instruction set

Machine State. A semantic state st is a tuple \(\langle I, L, R, F, M, MRs \rangle \) consisting of a sequence of instructions I, the current location L, registers R, an interpreter flag F, a memory M and a specification of available memory regions \( MRs \). The flag F characterizes the state of the rBPF interpreter. It may be i) a normal state, written \(F_n\); ii) a final state, written \(F_t\); iii) or an error state, written \(F_e\). An error state \(f\in F_e\) means that the defensive checks of the interpreter have detected that an invalid behavior is about to occur.

A memory region \(mr=\langle start, size, p, ptr\rangle \in MRs \) associates a permission \(p \in \{ Readable , Writable \}\) to the address range \([start, start + size)\). We make the link between concrete physical addresses and the CompCert memory model using the pointer ptr (= Vptr b 0) where the block b is the abstract representation of the address start. We write I(L) for the instruction located at the program counter L. R[r] retrieves the value of the register r in the register map R. Functions alu and cmp reuse the CompCert’s operators over the val type. The alu function returns \(\emptyset \) if an error occurs, e.g., division by zero. Functions load and store are those of CompCert’s memory model (see Sect. 3).

$$ \begin{array}{l} \mathbf {alu}: op \rightarrow val \rightarrow val \rightarrow option \; val \qquad \mathbf {cmp}: cmp \rightarrow val \rightarrow val \rightarrow bool\\ \mathbf {load}: chk \rightarrow mem \rightarrow block \rightarrow Z \rightarrow option \; val \\ \mathbf {store}: chk \rightarrow mem \rightarrow block \rightarrow Z \rightarrow val \rightarrow option \; mem \end{array} $$

Dynamic Checks. Function \(check\_alu\) dynamically checks the validity of an arithmetic to avoid div-by-zero and undefined-shift errors. For division instructions, \(check\_alu\) mandates the second argument to be non-zero. For arithmetic and logical shift instructions, the second argument has to be below \(n\in \{32,64\}\) depending on whether the ALU instruction operates on 32 or 64 bit operands. For simplicity, the paper only considers 64-bit ALU instructions but CertrBPF also has the 32-bit variants.

$$\begin{aligned}&check\_alu(op, v) \overset{\text {def}}{=} \left\{ \begin{array}{ll} v \ne 0 &{}\; if \; op \in \{div, mod\}\\ 0 \le v < n &{}\; if \; op \in \{lsh, rsh, arsh\}\\ true &{}\; otherwise\\ \end{array} \right. \end{aligned}$$

Function \(check\_mem\) returns a valid pointer \(( Vptr \; b \; o\!f\!s)\) if there exists a unique memory region mr in \( MRs \) such that i) the permission mr.perm is at least Readable for \(\mathtt {Load}\) and \( Writable \) for \(\mathtt {Store}\), i.e., \(mr.perm \ge p\); ii) the offset \(o\!f\!s\) is aligned, i.e., \(o\!f\!s\% Z(chk) = 0 \); iii) in bounds, i.e., \(o\!f\!s\le max\_unsigned - Z(chk)\), iv) and the interval \([o\!f\!s, hi\_o\!f\!s)\) is in the range of mr. Otherwise, \(check\_mem\) returns the null pointer \( Vnullptr \). The function Z(chk) maps memory chunks byte, halfword, word and double to 1, 2, 4, and 8, respectively.

$$\begin{array}{l} check\_mem(p, chk, addr, MRs ) \overset{\text {def}}{=} {\textbf {if}} \; \exists ! \; mr \in MRs , \; b.\\ \quad {\textbf {let}} \; o\!f\!s= addr - mr.start \;{\textbf {and}} \; hi\_o\!f\!s= o\!f\!s+ Z(chk)\; {\textbf {in}}\\ \qquad (mr.ptr == Vptr \; b \; 0) \; \wedge \; (mr.perm \ge p) \; \wedge \; (o\!f\!s\% Z(chk) == 0) \; \wedge \\ \qquad \quad (o\!f\!s\le max\_signed - Z(chk)) \; \wedge \; (0 \le o\!f\!s\wedge hi\_o\!f\!s< mr.size))\\ \qquad {\textbf {then}} \; Vptr \; b \; o\!f\!s\; {\textbf {else}}\; Vnullptr \end{array}$$

Semantics. Functions interp and sem formalize the implementation of our proof model \(M_p\) in the Coq proof assistant by defining a monadic interpreter of rBPF. The top-level recursion interp processes a (monotonically decreasing) fuel argument and a state s. The function sem processes individual instructions \(I(L_{pc})\). \( MRs \) and I are read-only. During normal execution, the flag remains \(F_n\). If the flag turns to \(F_t\) or \(F_e\) while processing an instruction, execution stops. For instance, if fuel reaches zero, the flag turns to \(F_e\). We write s.F for the value of field F in record s and \(s\{F=v\}\) updates it to v.

figure b

Result \(\emptyset \) marks transitions to crash states that are proved unreachable given our carefully crafted definitions of the check_alu and check_mem functions. Note that the interpreter interp does not check the range of branching offsets (i.e., \(\texttt {0<=s.L< length(s.I)}\)) and register-out-of-bounds. This properties are statically verified, once and for all, by the verifier of Sect. 8.

Exit terminates the program with flag \(F_t\). The \( Call \) instruction selects (using \( bpf\_get\_call \)) the trusted system API service designated by an immediate number imm. It then calls the chosen service if available (i.e., not a null pointer). Unconditional jump Ja increments the pc by \(o\!f\!s\) and a conditional Jump does so when cmp(csrcdest) holds. For an arithmetic operation \(\mathtt {Alu}\,op\, dst\, src\), \(check\_alu\) first checks the validity of op with source src, evaluates op against destination dst using alu, stores the result v in register dst. For simplicity, we omit the case of immediate srcs. If the result is \(\emptyset \), so becomes the monadic state (undefined behavior). Our definition of \(check\_alu\), and well-formedness conditions (see Sect. 5.1) ensures that this will never happen and that, in case of error, the execution terminates with flag \(F_e\). Similarly, the semantics of memory instructions (Load-Store) validates memory accesses using the \(check\_mem\) function. Its definition ensures the absence of undefined behaviors.

5.1 Proof of Software-Fault Isolation

Our proof model \(M_p\) formalizes the semantics of rBPF. It is implemented in Coq using Gallina. Assessing its correctness consists of proving two essential properties: i) the well-formedness of the virtual machine’s state, that is, its registers, memory and verifier invariants, and ii) software-fault isolation, that is, the isolation of all transitions to a crash state \(\emptyset \) using runtime safety checks (e.g., \(check\_mem\)), ergo the impossibility of a transition to an undefined behavior.

The register invariant states that all registers contain 64-bit integer values. This rules out 32-bit integers, \( Vundef \) but also pointers and floating-point numbers, for which the \(\mathbf {alu}\) function may be undefined.

Definition 1 (register__inv)

\(\forall r \in registers. \exists l. R[r] = Vlong \; l\)

As expected, the memory consistency invariant is a bit more elaborate. It states that each CompCert memory region mr register 8-bit integer blocks b of memory m, designated by a pointer mr.ptr to the 32-bit physical mr.start address of b, the 32-bit mr.size of b and at least Readable permissions mr.perm across [0, size). Finally, every two regions point to disjoint physical address spaces in m (as per CompCert’s memory regions for \(mr'.ptr \ne mr.ptr\)).

Definition 2 (memory__inv)

\(\forall mr \in MRs , \; m. \; \exists b, start, size\). s.t.

\(\begin{array}{l} mr.ptr = Vptr \; b \; 0 \; \wedge \; Mem.valid\_block \; m \; b \; \wedge \; is\_byte\_block \; b \; m \; \wedge \\ mr.start = Vint \; start \; \wedge \; mr.size = Vint \; size \; \wedge \; mr.perm \ge Readable \; \wedge \\ Mem.range\_perm \; m \; b \; 0\; (Int.unsigned \; size) \; Cur \; mr.perm \; \wedge \\ (\forall mr' \in MRs , mr' \ne mr \rightarrow mr'.ptr \ne mr.ptr) \end{array}\)

Linux eBPF has a verifier to statically analyze eBPF programs and only accept those which are free of undefined behaviors. Our CertrBPF’s verifier, introduced in Sect. 8, ensures the weaker invariant given by Definition 3. The invariant stipulates the minimal pre-condition so that the interpreter can safely run a sequence of instructions I. More precisely, the invariant states that each instruction I[i] references registers within the range [0, 10] and that the target of every jump instruction is within the program range i.e., \(0 \le i+o\!f\!s+1 \le length(I) - 1\).

Definition 3 (verifier_inv)

\(\forall i, \; I, \; o\!f\!s. \; 0 \le i \le length(I) - 1 \rightarrow \)

\(\begin{array}{l} 0 \le get\_dst(I[i]) \le 10 \; \wedge 0 \le get\_src(I[i]) \le 10 \; \wedge \\ ((I[i] = Ja \; o\!f\!s\vee I[i] = Jump \; \_\_ \; \_\_ \; \_\_ \; o\!f\!s) \rightarrow 0 \le i+o\!f\!s+1 \le length(I) - 1) \end{array}\)

These three invariants implement well-formedness as proposed in Sect. 4. Therefore, the following Coq Theorem sem_preserve_inv proves Theorem 1 and states that well-formedness is preserved by the interp function. Similarly, Theorem inv_ensure_no_undef proves Theorem 2. This proves that the dynamic checks of the model \(M_p\) are sufficient to ensure the absence of error. In particular, all memory accesses are valid and performed within the dedicated memory regions. As a result, our model ensures software fault isolation. The corollary of Theorems sem_preserve_inv and inv_ensure_no_undef is that our virtual machine, obtained by refinement of the proof model, will always isolate code from other memory regions of the operating system and never crash it.

figure c

6 A Synthesis-Oriented eBPF Interpreter

The coding style of the proof model \(M_p\) is quite different from the original RIOT implementation in C and lacks optimizations used in the latter to improve runtime performance. The synthesis model \(M_s\) firstly refines \(M_p\) into an optimized, safe and behaviorally equivalent monadic model which is then automatically transformed into an effectful implementation model \(M_c\) using \(\partial x\).

Synthesis Model \(M_s\). \(M_s\) refines our proof model by following the principle “make \(M_s\) as close as possible to the expected target C code". \(M_s\) also refines Coq types because each Coq inductive type may correspond to several C types (e.g., Vint/Vlong to signed or unsigned, 32-bit or 64-bit). The case of Vptr is particularly delicate, as the target type contextually relies on bit-size and signedness. To sort this out, we rename Coq types to match the correct C type. For example, \(val64\_t, valu32\_t\), \( vals32\_t\) are Val types mapped to unsigned long long, unsigned int and int, respectively.

Equivalence. Both \(M_p\) and \(M_s\) use the same monadic state st as in Sect. 5. Hence, the simulation relation \(R \subseteq st \times st\), required by Theorem 3, is equality. As a result, we prove the stronger result that both \(interp: nat \rightarrow M \; unit\), the \(M_p\) interpreter, and \(interp\_dx: nat \rightarrow M \; unit\), the \(M_s\) interpreter, denote the exact same function.

figure d

\(\partial x\) configuration and Implementation model \(M_c\). To extract the implementation model, we supply \(\partial x\) with our monad M and a mapping relation from Gallina to C, Table 1.

Table 1. Mapping relation from Gallina to C

Inductive types map to C types, e.g., reg to \(unsigned \;int\) (note that a many-to-one relation from Gallina to C is legal). Gallina constructs and constant functions map to C operators and constants, e.g.,\( Val.addl \)’ to ‘\(+\)’, ‘\( Int.repr (-2)\)’ and ‘true’ to ‘\(-2\)’ and ‘1’, etc. Gallina functions map to C functions. For any function operating the monadic state, the target C function has an additional argument st of type \(struct \; state *\) which corresponds to the implicit state of the monad. Gallina’s match-pattern translates to C’s switch-case, etc.

Code Extraction with \(\partial x\). The extracted C implementation preserves the structure of the original Gallina code, and the extracted C functions directly operate on actual memory locations as CompCert memory operations map to C expressions with a dereference. Consider the example of the step_mem_st_reg function.

figure e

CompCert’s Byte int8_t is mapped to unsigned char. Constructs op_BPF_STXW, BPF_ ILLEGAL_MEM and Writable are respectively mapped to ‘99‘, ‘-2‘ and ‘2U‘. The constant function eq_ptr_null is translated into an operation to check whether a pointer is null. The ‘match opcode_st with’ is extracted to ‘switch (opcode_st) case’. Functions step_mem_st_reg, check_mem and store_mem_reg in C have an additional monadic argument st.

figure f

7 Simulation Proof of the C rBPF Virtual Machine

In this section, we explain how to establish Theorem 4 for the Clight code of our virtual machine, derived from \(\partial x\), and compiled into a Clight AST in Coq using the clightgen tool.

Simulation Relation. A crucial ingredient of Theorem 4 is the simulation relation between the Gallina state monad and the Clight state which is essentially made of a CompCert memory. The Gallina state comprises a CompCert memory that models the various memory regions available to the rBPF program. This memory may also contain other blocks that are not modified by the virtual machine but represent other kernel data-structures. The simulation relation stipulates that such blocks also exist in the Clight memory and have the same content. The Clight memory contains additional blocks (i.e., \(state\_block\), \(ins\_block\) and \(mrs\_block\)) to model the other fields of the Gallina state. The layout and content of those blocks are depicted in Fig. 3.

Fig. 3.
figure 3

Simulation relation R between \(st_{rbpf}\), left, and rBPFClight, right.

Solid arrows in Fig. 3 are simulation relations between \(state\_block\) and \(st_{rbpf}\). Solid lines are the equalities between the rBPF memory m and blocks in rBPFClight memory. Dashed lines indicate relations of pointers to blocks in CompCert memory. The encoding exploits the fact that each field of the Gallina state has a known length. Thus, every field can be encoded as a continuous sub-block. As a result, the program counter is obtained from the first 4 bytes: loading a memory chunk of type \( Mint32 \) at offset 0 retrieves the pc field of the Gallina state. The next 4 bytes encode the enumerated type flag. Here, each constructor of type flag is assigned an integer. The next \(11 \times 64\) bits are used to encode the register bank of the Gallina state.

$$\begin{aligned}&Rs (state, state\_block, m) \overset{\text {def}}{=} \left\{ \begin{aligned}&st_{rbpf}.pc= & {} \; load \; Mint32 \; m_{clight}\; state\_block \; 0\\&st_{rbpf}.flag= & {} \; load \; Mint32 \; m_{clight}\; state\_block \; 4\\&st_{rbpf}.R0= & {} \; load \; Mint64 \; m_{clight}\; state\_block \; 8\\&\ldots \\ \end{aligned} \right. \end{aligned}$$

The next elements of the Clight block represent the lists of instructions and of memory regions. In a functional language, lists are potentially of unbounded length and have a polymorphic type. Here, our lists always have fixed lengths and elements of fixed size. As a result, a list is directly encoded by a field specifying its length followed by a pointer to its memory block. The elements of the list are stored continuously in the pointed block.

Systematic Proof of Simulation. Since the \(\partial x\) tool is syntax-directed, there is a systematic correspondence between the source Gallina and the target C code. We exploit this property to design a minimal Clight logic geared toward our simulation proof. Our Clightlogic generalizes the translation validation theorem (Theorem 4) to accommodate Gallina functions and C functions with multiple arguments. In that case, we have a precondition which states that the Gallina and C arguments are linked pairwise by a refinement relation. Most of the arguments are numeric values and, in this case, the refinement relation states that the Gallina and C values are the same. The Clightlogic also provides a syntax-directed proof principle for each pair of Gallina/C syntactic construct. For instance, the \( bindM \) operator translates to a sequence in the C code. Also, the result of a Gallina function call is bound to a local variable in C. Moreover, the local variable v below stands for the monadic state in C and points to the state memory block.

$$ \partial {x}(bindM\; f\; (\lambda x. g)) = (vx = f_C(v) ; g_C(v,vx)) $$

To exploit this pattern, our invariants take the form of an association list mapping each local variable to a set of C values that is obtained by partially evaluating a refinement relation with the Gallina value computed by the function (Fig. 3). To evaluate f, one needs to have a refinement relation Rs between the Gallina state st and the C value of v in memory m. Now, suppose that \(f st = \lfloor r,st' \rfloor \). Since \(f_C\) is a correct refinement of f, relations \(Rs(st',v,m')\) and Rr(rx) hold for the value x of the local variable vx in the current environment. We conclude by mapping \(vx \mapsto Rr\; r\) and use this invariant to refine g by \(g_C\).

The translation validation theorem proves a forward simulation relation from Coq to Clight. A backward simulation relation can be constructed as Gallina programs are functions and Clight is determinate.

8 CertrBPF Verifier

Linux eBPF’s compiler and runtime system do not enforce type or memory safety. Instead, safety is verified prior to execution using a static analyzer that checks programs validity. As both the size and complexity cannot fit the requirements of an MCU architecture, CertrBPF instead provides a simple (linear time) but formally verified verifier, CertrBPF-verifier, which ensures the invariant \(verifier\_inv\) (Definition 3). Accordingly, it scans an input rBPF program (i.e., a list of 64-bit bytecode instructions) and rejects it when: i) a source or destination register is greater than 10. ii) the offset of a jump instruction is out of the instruction sequence bounds. iii) or the last instruction is not the Exit instruction (opcode 0x95).

Static verification of these properties allows the interpreter to skip unnecessary dynamic checks. Our verifier adopts the same end-to-end verification method as the interpreter, Sect. 4. The virtual machine state in CertrBPF-verifier is a strict subset of the interpreter’s state: \(st_{v} = \langle I, M \rangle \) consists of a sequence of instructions I and a memory M.

figure g

Theorem \(verifier\_well\_formedness\_and\_safety\) proves both Theorem 1 and Theorem 2. The verifier has the following properties: i) no assumption (every state is well-formed); ii) never crashes (safety); iii) never modifies the VM state. In addition, the Coq theorem \(verifier\_imply\_inv\) states that if the verifier returns true, \(verifier\_inv\) holds. Considering that the verifier’s proof and synthesis models are exactly the same, the simulation relation \(R_{v} \subseteq st_{v} \times st_{v}\) required by Theorem 3 is equality. CertrBPF-verifier reuses the Clightlogic to prove the simulation proof of its C implementation.

9 Evaluation: Case Study of RIOT’s Femto-Containers

We integrate CertrBPF as a drop-in replacement for the existing non-verified module optimized for size (vanilla-rBPF) in the IoT operating system RIOT to provide the expected femto-container functionalities [39].

Implementation. The proof model of the interpreter (Sect. 5) consists of 2.4k lines of Coq code and the corresponding isolation proof (Sect. 5.1) is more than 4.8k lines long. The synthesis model, Sect. 6, is approx. 3.2k lines long and the equivalence theorem is completed by 0.6k proof code. The final step (Sect. 7) includes 10.8k translation validation proofs between the Gallina specification and the extracted Clight model. As for the CertrBPF verifier (Sect. 8), the proof and synthesis models sport 1.4k lines of Coq code. The corresponding proofs are more than 0.5k long and the last simulation proof is about 8.3k long. In addition, the Clightlogic implementation has 4.4k lines of Coq code.

Experimental Evaluation Setup. Our experimental objects are the original non-verified rBPF interpreter (i.e., vanilla-rBPF) and the automatically extracted and verified CertrBPF interpreter (without RIOT’s API). We carry out our measurements on a selected set of popular, commercial, off-the-shelf low-power IoT hardware, representative of modern 32-bit micro-controller architectures and boards: i) Nordic nRF52840 (Arm Cortex-M); ii) Espressif WROOM-32 (Espressif ESP32); iii) Sipeed Longan Nano GD32VF103CBT6 (RISC-V). All code is compiled with GCC using size optimization enabled and the -foptimize-sibling-calls GCC option to remove all tail-recursive calls and thus bound the stack size. This is critical to our isolation theorem as it relies on the implicit CompCert assumption that the stack cannot overflow. To avoid a possible mismatch between the CompCert semantics and the GCC semantics, we also pass the following options: i) -fwrapv, -fwrapv-pointer mean that both signed and pointer arithmetic wrap around according to the two’s-complement encoding; ii) -fno-strict-aliasing means that there is no aliasing assumption.

Results. We first evaluate the memory footprint of the CertrBPF interpreter, compared to vanilla-rBPF. We measure i) Flash size: all read-only data, including the actual code; ii) Stack: the approximate ram used for stack space; iii) Context: the static RAM. In terms of Flash, our measurements show that CertrBPF actually reduces the footprint by 47% on RISC-V and by 35% on ESP32, and a 10% decrease on Cortex-M. In terms of stack requirements, CertrBPF reduces the footprint by 33% on Cortex-M, by 22% on RISC-V, and by 4% on ESP32. The context memory, however, increases from 92B to 144B on all platforms.

Fig. 4.
figure 4

Time per instructions on the Cortex-M4 platform

Next, we micro-benchmark the performance of core operations: single instructions from the arithmetic logic unit (ALU), for memory access (MEM) and branch instructions, with a mix of register and immediate value for the operands, Fig. 4. These results are averages over 1000 single identical instruction calls with a single return statement to make the application exit.

Finally, we benchmark the performance of actual IoT data processing, hosted in a femto-container with RIOT running on our selected hardware. In this use case, a sliding window average is performed within the femto-container, on available sensor data points. Figure 5 shows the performance we measured depending on the size of the window. We use this as blueprint for computation load scaling.

Fig. 5.
figure 5

Sliding window average on Cortex-M, ESP32, and RISC-V.

Key Take-Away. We observe that CertrBPF generally decreases the memory footprint. One reason is that calls to the RIOT API are currently not supported by CertrBPF. We observe, Fig. 4, that the execution slow-down is acute for Branch instructions, on Cortex-M. However, on all other platforms (RISC-V, ESP32 and Cortex-M), our micro-benchmarks show that most instructions enjoy speed-up with CertrBPF compared to vanilla-rBPF. This behavior is also visible in our sensor data processing benchmark, Fig. 5, where CertrBPF performs better than vanilla-rBPF on three platforms. All in all, CertrBPF gains both security and reduces memory footprint as well as execution time.

10 Related Works

Methodologies for Systems and Compilers Verification. The verification of compilers [18], static analyzers [16], and operating systems [12, 17] have been the subjects of vast development and verification efforts due to the sheer code size of the artifacts at stake. These full-scale case studies gave rise to new strategies and methodologies to address the challenge of verifying large software. One such approach is Cogent [35] which aims at developing verified applications on top of the SeL4 [17] micro-kernel. Cogent [35] consists of a functional language with linear types to specify source programs and produces C code with Isabelle/HOL proof information. It provides a framework to prove that the extracted C code refines a high-level Isabelle/HOL functional correctness specification in the Isabelle/HOL proof assistant. Our method differs from co-specification in Cogent in that it is direct: it directly translates Coq specifications into C code and performs the end-to-end verification in Coq. CertiKOS [12] uses a multi-layered, refinement-based, and modular definition of a micro-kernel from its low-level memory model to its user-level interface and services. It is adopted in SeKVM [22], a layered Linux KVM hypervisor architecture for multiprocessor hardware. The CompCert project [18] adopted this “divide-and-conquer” strategy to decompose the verification of a full-scale ANSI C compiler into that of its successive transformations from source program to machine code, compositionally verifying each of the translation steps bisimilar. Its related static analyser, Verasco [16], employs static analysis of CompCert C code using a verified core abstract interpreter with composable abstract domains. Our problem statement is methodologically simpler: to build a safe and small VM that interprets rBPF virtual instructions on networked micro-controllers. We choose the radical approach of proof-oriented programming (à la Low\(^\star \)  [34], Vale [5]) to prove an rBPF interpreter embedded in Coq correct and to directly extract verified code from its definition.

Background on BPF and Its Verified Implementations. Mogul et al. [26] introduce a stack-based virtual machine to interpret packet filters into the BSD kernel that BPF extended to 32-bit instructions. BPF gained adoption in the Linux community and became eBPF (extended BPF), a virtual 64-bit RISC-like architectures. To our knowledge, verification of BPF runtime systems has mainly focused on JIT translation for operation on micro-kernels. Myreen [28] verifies a JIT compiler targeting x86 for a stack language using the HOL4 proof assistant. The generated code only preserves the semantics of the source code but does not ensure any isolation property. Porncharoenwase et al. [33] use CompCert to extract an OCaml translator from BPF to assembly code, verified using the proof assistant Coq, using the OCaml runtime, an assembler, and a linker as TCB. Van Geffen et al. [11] present an optimized JIT compiler for Linux BPF with automated static analysis onboard, assuming offline verification using the Linux BPF verifier as TCB. For field deployment on networks of micro-controllers (IoT), all the above approaches would require a trusted, offline BPF verifier and, additionally, a secure upload protocol to sign verified scripts and perform authenticated uploads on target devices, which motivates our approach to use a fault-proof virtual machine instead.

Background on Verified Virtual Machines. Lochbihler [23] presents the verified implementation of a virtual machine modeling the semantics, memory model and byte-code semantics of Java, all by using the proof methodology of translation validation [18, 32]. Desharnais and Brunthaler [7] propose the formal verification of an optimized and secure Javascript interpreter in Isabelle/HOL. Its proof methodology is based on concepts of bisimulation. The interpreter targets optimal security and run-time performance. To target MCU devices, our rBPF VM instead seeks optimal run-time memory footprint, to support the expected capability of dynamically running several isolated services on a small device with shared memory. Zhang et al. [40] present a different and ambitious workflow using the deductive programming environment Why3 [9] to specify a virtual machine of Etherium byte-code (EVM) and verify functional correctness of smart contracts against it. The EVM is extracted to OCaml binary code, yielding a TCB consisting of the OCaml runtime and the implementation of Eth’s protocols.

Background on Converting Gallina Programs into Executables. Just as the proof-oriented approach advocated by dependently-typed functional languages like F\(^\star \) mentioned in Sect. 2, there are various alternatives to \(\partial x\) for extracting executables from Gallina programs. To begin with, Coq comes with a builtin extraction mechanism [21] that generates OCaml, Haskell or Scheme. This path has a rather large TCB (Coq extraction and a compiler). CertiCoq [1] is an ongoing project aiming at generating CompCert C code from Gallina using a specific IR and several passes. Once this effort is completed, it will allow one to rely on a small TCB. Œuf [27] is another tool to compile Gallina to C. It considers a carefully chosen subset of Gallina to tackle the tricky issue of verifying the reflection of Gallina into an AST. Both CertiCoq and Œuf, however, require a garbage collector and define how Coq inductives are represented at runtime. Codegen [36] converts Gallina to C with the goal of maximizing performance by, e.g., allowing the user to control how Coq values are represented at runtime. Rupicola [31] considers an original and promising approach which regards a compiler as a partial decision procedure: it consists of a proof search procedure, which may fail, or else exhibit a target program in bedrock2 (a C-like low-level language AST embedded in Coq) with a proof of equivalence. It has, at present, only been tested for small algorithms. We chose to use \(\partial x\) for its simplicity and because it does not increase our TCB. It shares with Codegen the capability to configure the representation of values. Unlike Codegen, it produces C code that is structurally identical to source code. This direct and traceable translation simplifies the verification of generated code w.r.t. source programs, and facilitates source program optimisations.

11 Conclusion and Future Works

This paper uses a refinement methodology to directly derive a verified C implementation of rBPF, the implementation of BPF hosted by the RIOT operating system, from a Gallina specification in Coq. All the refinement steps are mechanically verified using the Coq proof assistant to minimize the TCB. We prove our rBPF virtual machine to isolate software faults and not to produce runtime errors. Performances are at par with the vanilla rBPF implementation in RIOT.

Our future works aim at instantiating our proof workflow to a (fault-isolating) JIT compiler, one challenge being that Linux’s approach of using a verifier will not be feasible on resource-constrained devices, and another being that certain operations might only be expressible in assembly code. This calls for further studies on ways to substantially improve the efficiency of our VM.

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Acknowledgments

The authors wish to thank the anonymous reviewers for their feedback and suggestions. This work is partly funded by Inria Challenge RIOT-fp, the ANR/BMBF project TinyPART, and the H2020 project Sparta.

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The source code and proofs of our virtual machine, its generated code and benchmark data are available on https://gitlab.inria.fr/syuan/rbpf-dx/-/tree/CAV22-AE.

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Yuan, S., Besson, F., Talpin, JP., Hym, S., Zandberg, K., Baccelli, E. (2022). End-to-End Mechanized Proof of an eBPF Virtual Machine for Micro-controllers. In: Shoham, S., Vizel, Y. (eds) Computer Aided Verification. CAV 2022. Lecture Notes in Computer Science, vol 13372. Springer, Cham. https://doi.org/10.1007/978-3-031-13188-2_15

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  • Print ISBN: 978-3-031-13187-5

  • Online ISBN: 978-3-031-13188-2

  • eBook Packages: Computer ScienceComputer Science (R0)