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Formal Methods for Minimizing the DHOSA Trusted Computing Base Greg Morrisett, Harvard University with A.Chlipala, P.Govereau, G.Malecha, G.Tan, J.Tassorati,

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Presentation on theme: "Formal Methods for Minimizing the DHOSA Trusted Computing Base Greg Morrisett, Harvard University with A.Chlipala, P.Govereau, G.Malecha, G.Tan, J.Tassorati,"— Presentation transcript:

1 Formal Methods for Minimizing the DHOSA Trusted Computing Base Greg Morrisett, Harvard University with A.Chlipala, P.Govereau, G.Malecha, G.Tan, J.Tassorati, & J.B.Tristan 1

2 DHOSA Technologies We are investigating a variety of techniques to defend hosts:  Binary Translation & Instrumentation  LLVM & Secure Virtual Architecture  New Hardware architectures How can we minimize the need to trust these components? 2

3 The role of formal methods  Ideally, we should have proofs that the tools are “correct”.  The consumer should be able to independently validate the proofs against the working system.  This raises three hard problems:  We need formal models of system components.  We need formal statements of “correctness”.  We need proofs that our enforcement/rewriting/analysis code (or hardware) are correct. 3

4 Some of our activities  Tools for formal modeling of machine architectures  Domain-specific languages embedded into Coq.  Give us declarative specs of machine-level syntax & semantics.  Give us executable specifications for model validation.  Give us the ability to formally reason about machine code.  Tools for proving correctness of binary-validation  Specifically, that a binary will respect an isolation policy.  e.g., SFI, CFI, XFI, NaCL, TAL, etc.  Tools for proving correctness of compilers.  New techniques for scalable proofs of correctness.  New techniques for legacy compilers. 4

5 Modeling Machine Architectures  Real machines (e.g., Intel’s IA64) are messy.  Even decoding instructions is hard to get right.  The semantics are not explained well (and not always understood.)  There are actually many different versions.  Yet to prove that a compiler or analysis or rewriting tool is correct, we need to be able to reason about real machine architectures.  And of course, we don’t just want Intel IA64.  Need IA32, AMD, ARM, …  And of course the specialized hardware that DHOSA is considering! 5

6 Currently  Various groups are building models of machines.  ACL2 group doing FP verification  Cambridge group studying relaxed memory models  NICTA group doing L4 verification  Inria group doing compiler verification  However, none of them really supports everything we need: 1. declarative formulation – crucial for formal reasoning 2. efficiently executable – crucial for testing and validation 3. completeness – crucial for systems-level work 4. reuse in reasoning – crucial for modeling many architectures 6

7 Our Approach  Two domain-specific languages (DSLs)  One for binary de-coding (parsing): bits -> ASTs  One for semantics: ASTs -> behavior  The DSLs are inspired by N. Ramsey’s work.  Sled and λ -RTL.  Ramsey’s work intended for generating compiler back-ends.  Our focus is on reasoning about compiler-like tools.  The DSLs are embedded into Coq.  lets us reason formally (in Coq) about parsing, semantics.  e.g., is decoding deterministic?  e.g., will this binary, when executed in this state, respect SFI?  the encoding lets us extract efficient ML code (i.e., a simulator) 7

8 Decoding?? 8

9 Yacc in Coq via Combinators Definition CALL_p : parser instr := "1110" $ "1000" $ word @ (fun w => CALL (Imm_op w) None) || "1111" $ "1111" $ ext_op_modrm (str ”010” || str ”011”) @ (fun op => CALL op None) || "1001" $ "1010" $ halfword $$ word @ (fun p => CALL (Imm_op (snd p)) (Some (fst p))). 9

10 X86 Integer Instruction Decoder Definition instr_parser := AAA_p || AAD_p || AAM_p || AAS_p || ADC_p || ADD_p || AND_p || CMP_p || OR_p || SBB_p || SUB_p || XOR_p || ARPL_p || BOUND_p || BSF_p || BSR_p || BSWAP_p || BT_p || BTC_p || BTR_p || BTS_p || CALL_p || CBW_p || CDQ_p || CLC_p || CLD_p || CLI_p || CMC_p || CMPS_p || CMPXCHG_p || CPUID_p || CWD_p || CWDE_p || DAA_p || DAS_p || DEC_p || DIV_p || HLT_p || IDIV_p || IMUL_p || IN_p || INC_p || INS_p || INTn_p || INT_p || INTO_p || INVD_p || INVLPG_p || IRET_p || Jcc_p || JCXZ_p || JMP_p || LAHF_p || LAR_p || LDS_p || LEA_p || LEAVE_p || LES_p || LFS_p || LGDT_p || LGS_p || LIDT_p || LLDT_p || LMSW_p || LOCK_p || LODS_p || LOOP_p || LOOPZ_p || LOOPNZ_p || LSL_p || LSS_p || LTR_p || MOV_p || MOVCR_p || MOVDR_p || MOVSR_p || MOVBE_p || MOVS_p || MOVSX_p || MOVZX_p || MUL_p || NEG_p || NOP_p || NOT_p || OUT_p || OUTS_p || POP_p || POPSR_p || POPA_p || POPF_p || PUSH_p || PUSHSR_p || PUSHA_p || PUSHF_p || RCL_p || RCR_p || RDMSR_p || RDPMC_p || RDTSC_p || RDTSCP_p || REPINS_p || REPLODS_p || REPMOVS_p || REPOUTS_p || REPSTOS_p || REPECMPS_p || REPESCAS_p || REPNECMPS_p || REPNESCAS_p || RET_p || ROL_p || ROR_p || RSM_p || SAHF_p || SAR_p || SCAS_p || SETcc_p || SGDT_p || SHL_p || SHLD_p || SHR_p || SHRD_p || SIDT_p || SLDT_p || SMSW_p || STC_p || STD_p || STI_p || STOS_p || STR_p || TEST_p || UD2_p || VERR_p || VERW_p || WAIT_p || WBINVD_p || WRMSR_p || XADD_p || XCHG_p || XLAT_p. 10

11 Parsing Semantics  The declarative syntax helps get things right.  we can literally scrape manuals to get decoders.  though it’s far from sufficient – manuals have bugs!  It’s possible to give a simple functional interpretation of the parsing combinators (a la Haskell).  parser T := string -> FinSet(string * T)  Makes it very easy to reason about parsers and prove things like || is associative and commutative.  or e.g., that Intel’s manuals are deterministic (they are not).  But it’s not very efficient.  in essence, does backtracking search.  and is working at the bit level.  we want to be able to extract efficient code. 11

12 Proven Correct Parser Generators  So as in Yacc or other parser generator tools, we are compiling the DSL for syntax specification into an efficient program.  We use on-the fly calculation and memoization of parsing derivatives a la Brzozowski and more recently, Might & Darais.  In essence, lazily construct the DFA.  Importantly, we are able to prove the correctness of this translation within Coq.  To be honest, we’ve only done recognition, not parsing so far.  And are still working at the bit-level instead of byte level.  Bottom line: don’t have to trust that the “yacc” compilation is right. 12

13 Semantics The usual style for machines is a small-step, operational semantics. M(R 1 (pc)) = a parse(M,a) = i (M,R 1,i)  (M’,R 1 ’) (M,R 1 || R 2 || … || R n )  (M’,R 1 ’ || R 2 || … || R n ) This makes it easy to specify non-determinism and reason about the fine-grained behavior of the machine. But doesn’t really give us an efficient executable. Nor reusable reasoning. 13

14 Our approach Write a monadic denotational semantics for instructions: Definition step_AND(op1 op2:operand) := w1 <- get_op32 op1 ; w2 <- get_op32 op2 ; let res := Word32.Int.and w1 w2 in set_op32 op1 res ;; set_flag OF false ;; set_flag CF false ;; set_flag ZF (is_zero32 res) ;; set_flag SF (is_signed32 res) ;; set_flag PF (parity res) ;; b <- next_oracle_bit ; set_flag AF b 14

15 Reasoning versus Validation  The monadic operations can be interpreted as pure functions over oracles and machine states.  The monadic operations are essentially RTLs over bit-vectors.  The infrastructure can be re-used across a wide variety of machine architectures.  i.e., defining and reasoning about machine architecture semantics becomes relatively easy.  But we can extract efficient ML code for testing the model against other simulators & real machines.  e.g., in-place updates for state changes instead of functional data structures.  In the next talk, you’ll hear one way we can validate our semantics against other simulators. 15

16 Using the models: SFI  SFI is a simple kind of binary-rewriting for enforcing an isolation policy.  good baseline for us to study  mask the high-bits of every store/jump to ensure a piece of untrusted code stays in its sandbox.  tricky: must consider every parse of the x86 code.  by enforcing an alignment convention, ensures there’s only one parse.  security depends on the “checker” which verifies these properties.  Our goal: produce a proof that the checker only says “ok” on code which, when executed, respects the sandbox policy. 16

17 Thus far…  Focus: Formal methods for modeling real machines.  DSLs for instruction decoding, instruction semantics.  Yield both formal reasoning & efficient execution.  Allows us to prove correctness of binary-level tools like the SFI checker.  Another Focus: compiler correctness  Crucial for eliminating language-based techniques from TCB.  For example, the Illinois group’s secure virtual architecture depends upon the correctness of the LLVM compiler. 17

18 To Date  Gold standard was Leroy’s Compcert Compiler  (mildly) optimizing compiler for C to x86, ARM, PPC  models of these languages & architectures  proof of correctness  See J.Regher’s compiler bug paper at PLDI.  However:  machine models are incomplete, unvalidated  optimization at O1 levels but not O3  proofs are roughly 17x the size of the code! 18

19 Last Time Post Doc Adam Chlipala’s work on lambda-tamer:  compiler from core-ML to MIPS-like machine  transformations like CPS and closure-conversion  breakthrough: |proofs| ≈ |code|  clever language representations avoid tedious proofs about variables, scope, binding.  clever language semantics makes reasoning simpler, more uniform.  clever tactic-based reasoning makes proofs mostly automatic, and far more extensible. 19

20 Current Work:  We have built a version of LLVM where the optimizer is provably correct (see PLDI’11 paper).  to be fair, only intra-procedural optimizations  but includes global value numbering, sparse conditional constant propagation, advanced dead code elimination, loop invariant code motion, loop deletion, loop unrolling, and dead-store elimination.  The “proof” is completely automated.  in essence, we have a way to effectively prove that the input to the optimizer has the same behavior as the output.  or more properly, when we can’t, we don’t optimize the code.  The prover knows nothing about the internals of the LLVM optimizer.  so it’s easy to change LLVM, or add new optimizations. 20

21 LLVM Translation Validation LLVM front-ends LLVM Optimizer code generator equivalence checker 21

22 How do we do this?  Convert LLVM’s SSA-based intermediate language into a categorical value graph representation.  similar to circuit representations (think BDDs).  but incorporates loops by lifting everything to the level of streams of values.  allows us to reason equationally about both data and control.  similar to work of Sorin Lerner on PEGs.  Take advantage of category theory to normalize the input and output graphs, and check for equivalence.  this gives us many equivalences for free, such as common sub- expressions and loop-invariant computations.  but still need to normalize underlying scalar computations.  The key challenge is getting this to scale to big functions. 22

23 % of Functions Validated on all Opts. Fail: we fail to translate LLVM’s IR into our representation Alarm: we fail to validate the translation OK: we validate the translation and there are significant differences Boring: we validate but the differences are minimal 23

24 Quick Recap  DHOSA relies upon compilers, rewriting, analysis, and other software tools to provide protection.  Our goal is to increase assurance in these tools.  provide detailed formal models of machines  prove correctness of key components  find techniques for automating proofs  The hope is that these investments will pay off, not just for this project but others.  e.g., IARPA Stonesoup, DARPA CRASH 24


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