Artifact for CCS 24: SemPat: Using Hyperproperty-based Semantic Analysis to Generate Microarchitectural Attack Patterns

Paper (arXiV Version): https://arxiv.org/abs/2406.05403

Contact: Adwait Godbole, UC Berkeley ([email protected])

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Setup

Requirements

1. Scala and SBT: We have built/tested with OpenJDK 11, Scala 2.12.11 and SBT 1.4.2. Probably the easiest way to install these is using SDKMAN: https://sdkman.io/.

2. Z3: SECANT requires the Z3 SMT solver: https://github.com/Z3Prover/z3. We have tested with z3 version 4.8.8. The convenience script secant/get-z3-linux.sh downloads and installs z3, (run: source get-z3-linux.sh). The helper sub-script secant/setup-z3-linux.sh adds the Z3 binary/libraries to $PATH.

3. Python3: Benchmark scripts have been tested with Python3 (3.5+ should suffice) and require the tabulate package: pip install tabulate (for pretty-printing results).

Building from source

1. Run the command: sbt clean compile universal:packageBin from the secant directory.

2. The build targets are placed tool can be found in the secant/target/universal directory. The convienence script secant/unpack-secant.sh can be used to extract the binary. Please add the binary location secant/target/universal/secant-0.0.1/bin to your $PATH variable.

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Benchmarks

The benchmarks directory provides convenience scripts to run all the examples. There are 5 scripts:

1. bench_sv_kocher.py (for Figure 11)
2. bench_tg_bvssv2.py, bench_tg_bvcompre.py, bench_tg_synthetic.py (for Figure 12)
3. bench_sv_fpr.py (for Table 5)

Further the super-script bench_all.py calls all these scripts in order and combines the results into a single file (this may take 6-7 hours to run).

Each script can be called individually (from benchmarks) as: python3 <scriptname> <resultsdir> [--dryrun] where results are placed in <resultsdir>, and the --dryrun flag performs a dry-run without actually calling the tool. Running the script produces a table all_times.md in <resultsdir>.

We now list the scripts, with estimated runtime and results to validate.

Kocher's Modified Tests (Figure 11)

bench_sv_kocher (Figure 11): Run command python3 bench_sv_kocher.py bench_sv_kocher from benchmarks. Time estimate: ~3 hours.

This scripts perform verification using both hyperproperties and patterns for modified Kocher's benchmarks (Figure 11). In particular check that:

1. All generation times have reasonably precise order magnitude
2. Run-time with hyperproperties dominates that with patterns and that the results match.
3. The claim that larger platform complexity affects hyperproperty time, but does not affect pattern generation time.

Pattern Generation Scalability (Figure 12)

bench_tg_bvssv2 (Figure 12(a,b)): Run command python3 bench_tg_bvssv2.py bench_tg_bvssv2 from benchmarks. Time estimate: ~3 hours.

bench_tg_compre (Figure 12(c)): Run command: python3 bench_tg_compre.py bench_tg_compre. Time estimate: ~20 minutes.

bench_tg_synthetic (Figure 12(d)): Run command python3 bench_tg_synthetic.py bench_tg_synthetic from benchmarks. Time estimate: ~15 minutes.

These scripts perform pattern generation (and are used for) produce results for Figure 12. In particular check that:

1. All generation times have reasonably precise order magnitude.
2. Run-time with increasing set-index grows faster than that with way-index (Figure 12(a,b) with bench_tg_bvssv2).

False Positive Experiment (Table 5)

bench_sv_fpr (Figure 11): Run command python3 bench_sv_fpr.py bench_sv_fpr from benchmarks. Time estimate: ~5 minutes.

This scripts performs verification of a set of litmus tests using two versions of a pattern: one that is more coarse and one that includes finer predicates (see Section 7.3). Check that:

1. The results match Table 5: in particular, false positives (UNSAFE results) occur when using pattern F (Figure 13) with $K > \texttt{setindex}$.

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