fix(obfuscation): prevent crash and cap recursion in SQL/HTTP obfuscation by ajgajg1134 · Pull Request #2441 · DataDog/libdatadog

ajgajg1134 · 2026-09-01T15:03:17Z

Summary

Cap recursion depth for nested dollar-quoted SQL strings so pathologically nested input can no longer exhaust the stack and abort the process.
Fix a fallback path in URL obfuscation that could return an unparseable URL with userinfo still attached instead of stripping it.

Test plan

cargo nextest run -p libdd-trace-obfuscation (343 tests, incl. new regression tests for both fixes)
cargo fmt --check / cargo clippy -D warnings clean

🤖 Generated with Claude Code

…tion Cap recursion depth for nested dollar-quoted SQL strings so pathological nesting can no longer exhaust the stack and abort the process, and fix a fallback path in URL obfuscation that could return an unparseable URL with userinfo still attached instead of stripping it. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

github-actions · 2026-09-01T15:05:10Z

📚 Documentation Check Results

⚠️ 853 documentation warning(s) found

📦 `libdd-trace-obfuscation` - 853 warning(s)

Updated: 2026-09-01 18:51:36 UTC | Commit: 156b5df | missing-docs job results

github-actions · 2026-09-01T15:07:14Z

🔒 Cargo Deny Results

⚠️ 3 issue(s) found, showing only errors (advisories, bans, sources)

📦 `libdd-trace-obfuscation` - 3 error(s)

Show output

error[vulnerability]: Invalid pointer dereference in `fmt::Pointer` impl for `Atomic` and `Shared` when the underlying pointer is invalid
   ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:64:1
   │
64 │ crossbeam-epoch 0.9.18 registry+https://github.com/rust-lang/crates.io-index
   │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
   │
   ├ ID: RUSTSEC-2026-0204
   ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0204
   ├ Affected versions of `fmt::Display` dereference the underlying pointer. This causes a invalid pointer dereference e.g., when a pointer created with `Atomic::null` or `Shared::null`. `fmt::Debug` impls and pre-0.9 `fmt::Display` impls, which do not dereference pointers, are not affected by this issue.
   ├ Announcement: https://github.com/crossbeam-rs/crossbeam/pull/1276
   ├ Solution: Upgrade to >=0.9.20 (try `cargo update -p crossbeam-epoch`)
   ├ crossbeam-epoch v0.9.18
     └── crossbeam-deque v0.8.5
         └── rayon-core v1.12.1
             └── rayon v1.10.0
                 └── criterion v0.5.1
                     ├── libdd-common v5.2.0
                     │   ├── libdd-capabilities-impl v4.0.0
                     │   │   └── libdd-trace-utils v11.0.0
                     │   │       ├── libdd-trace-obfuscation v7.0.0
                     │   │       └── (dev) libdd-trace-utils v11.0.0 (*)
                     │   ├── libdd-trace-obfuscation v7.0.0 (*)
                     │   └── libdd-trace-utils v11.0.0 (*)
                     ├── (dev) libdd-trace-normalization v4.0.0
                     │   └── libdd-trace-utils v11.0.0 (*)
                     ├── (dev) libdd-trace-obfuscation v7.0.0 (*)
                     └── (dev) libdd-trace-utils v11.0.0 (*)

error[vulnerability]: h2 unbounded empty DATA frames
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:101:1
    │
101 │ h2 0.4.6 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0258
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0258
    ├ The h2 crate, used internally by hyper, had a flaw that would accept and queue empty DATA frames without limit.
      If streams were not actively drained, this could lead to unbounded memory usage, or a panic if the length overflows.
      
      Low severity.
      
      Patched in v0.4.16.
    ├ Announcement: https://github.com/hyperium/hyper/security/advisories/GHSA-q83h-524g-xf6h
    ├ Solution: Upgrade to >=0.4.16 (try `cargo update -p h2`)
    ├ h2 v0.4.6
      └── hyper v1.6.0
          ├── httpmock v0.8.0-alpha.1
          │   └── libdd-trace-utils v11.0.0
          │       ├── libdd-trace-obfuscation v7.0.0
          │       └── (dev) libdd-trace-utils v11.0.0 (*)
          ├── hyper-rustls v0.27.7
          │   └── libdd-common v5.2.0
          │       ├── libdd-capabilities-impl v4.0.0
          │       │   └── libdd-trace-utils v11.0.0 (*)
          │       ├── libdd-trace-obfuscation v7.0.0 (*)
          │       └── libdd-trace-utils v11.0.0 (*)
          ├── hyper-util v0.1.17
          │   ├── httpmock v0.8.0-alpha.1 (*)
          │   ├── hyper-rustls v0.27.7 (*)
          │   └── libdd-common v5.2.0 (*)
          ├── libdd-common v5.2.0 (*)
          └── libdd-trace-utils v11.0.0 (*)

error[unsound]: Rand is unsound with a custom logger using `rand::rng()`
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:189:1
    │
189 │ rand 0.8.5 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ unsound advisory detected
    │
    ├ ID: RUSTSEC-2026-0097
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0097
    ├ It has been reported (by [@lopopolo](https://github.com/lopopolo)) that the `rand` library is [unsound](https://rust-lang.github.io/unsafe-code-guidelines/glossary.html#soundness-of-code--of-a-library) (i.e. that safe code using the public API can cause Undefined Behaviour) when all the following conditions are met:
      
      - The `log` and `thread_rng` features are enabled
      - A [custom logger](https://docs.rs/log/latest/log/#implementing-a-logger) is defined
      - The custom logger accesses `rand::rng()` (previously `rand::thread_rng()`) and calls any `TryRng` (previously `RngCore`) methods on `ThreadRng`
      - The `ThreadRng` (attempts to) reseed while called from the custom logger (this happens every 64 kB of generated data)
      - Trace-level logging is enabled or warn-level logging is enabled and the random source (the `getrandom` crate) is unable to provide a new seed
      
      `TryRng` (previously `RngCore`) methods for `ThreadRng` use `unsafe` code to cast `*mut BlockRng<ReseedingCore>` to `&mut BlockRng<ReseedingCore>`. When all the above conditions are met this results in an aliased mutable reference, violating the Stacked Borrows rules. Miri is able to detect this violation in sample code. Since construction of [aliased mutable references is Undefined Behaviour](https://doc.rust-lang.org/stable/nomicon/references.html), the behaviour of optimized builds is hard to predict.
    ├ Announcement: https://github.com/rust-random/rand/pull/1763
    ├ Solution: Upgrade to >=0.10.1 OR <0.10.0, >=0.9.3 OR <0.9.0, >=0.8.6 (try `cargo update -p rand`)
    ├ rand v0.8.5
      ├── (dev) libdd-common v5.2.0
      │   ├── libdd-capabilities-impl v4.0.0
      │   │   └── libdd-trace-utils v11.0.0
      │   │       ├── libdd-trace-obfuscation v7.0.0
      │   │       └── (dev) libdd-trace-utils v11.0.0 (*)
      │   ├── libdd-trace-obfuscation v7.0.0 (*)
      │   └── libdd-trace-utils v11.0.0 (*)
      ├── (dev) libdd-trace-normalization v4.0.0
      │   └── libdd-trace-utils v11.0.0 (*)
      ├── libdd-trace-utils v11.0.0 (*)
      └── proptest v1.5.0
          ├── (dev) libdd-common v5.2.0 (*)
          └── (dev) libdd-tinybytes v1.1.2
              ├── (dev) libdd-tinybytes v1.1.2 (*)
              ├── (dev) libdd-trace-obfuscation v7.0.0 (*)
              └── libdd-trace-utils v11.0.0 (*)

advisories FAILED, bans ok, sources ok

Updated: 2026-09-01 18:51:25 UTC | Commit: 156b5df | dependency-check job results

datadog-datadog-prod-us1-2 · 2026-09-01T15:16:14Z

⚠️ Warnings

❌ Your PR has failed checks. Please review the issues below and take necessary action before merging.

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ℹ️ Info

No other issues found (see more)

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 76.92% (-0.03%)

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🔗 Commit SHA: 36c57ed | Docs | View more details | Give us feedback!}

pr-commenter · 2026-09-01T15:22:29Z

Benchmarks

Comparison

Benchmark execution time: 2026-09-01 19:02:42

Comparing candidate commit 36c57ed in PR branch andrew.glaude/noCrash with baseline commit 3c5ccac in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 25 metrics, 0 unstable metrics.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

🟩 = significantly better candidate vs. baseline
🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

Benchmark execution time: 2026-09-01 19:01:20

Comparing candidate commit 36c57ed in PR branch andrew.glaude/noCrash with baseline commit 3c5ccac in branch main.

Found 1 performance improvements and 0 performance regressions! Performance is the same for 34 metrics, 0 unstable metrics.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

🟩 = significantly better candidate vs. baseline
🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

scenario:redis/obfuscate_redis_string

🟩 execution_time [-5.269µs; -4.887µs] or [-14.968%; -13.883%]

Candidate

Candidate benchmark details

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
concentrator/add_spans_to_concentrator	execution_time	9.600ms	9.627ms ± 0.018ms	9.623ms ± 0.012ms	9.637ms	9.661ms	9.676ms	9.701ms	0.82%	1.076	1.278	0.19%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
concentrator/add_spans_to_concentrator	execution_time	[9.624ms; 9.629ms] or [-0.026%; +0.026%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
trace_buffer/1_senders/10us_delay	execution_time	60.040ms	60.082ms ± 0.033ms	60.072ms ± 0.007ms	60.084ms	60.141ms	60.209ms	60.243ms	0.28%	2.489	7.108	0.05%	0.002ms	1	200
trace_buffer/1_senders/10us_delay	throughput	14939.502op/s	14979.517op/s ± 8.175op/s	14982.067op/s ± 1.832op/s	14983.487op/s	14987.613op/s	14988.780op/s	14989.949op/s	0.05%	-2.484	7.081	0.05%	0.578op/s	1	200
trace_buffer/1_senders/1us_delay	execution_time	50.216ms	50.739ms ± 0.222ms	50.750ms ± 0.162ms	50.890ms	51.108ms	51.159ms	51.322ms	1.13%	-0.086	-0.476	0.44%	0.016ms	1	200
trace_buffer/1_senders/1us_delay	throughput	17536.455op/s	17738.221op/s ± 77.663op/s	17733.895op/s ± 56.442op/s	17793.582op/s	17866.033op/s	17916.761op/s	17922.719op/s	1.06%	0.105	-0.475	0.44%	5.492op/s	1	200
trace_buffer/1_senders/no_delay	execution_time	347.167µs	357.071µs ± 6.418µs	356.732µs ± 2.088µs	358.480µs	363.405µs	372.708µs	426.281µs	19.50%	6.569	66.413	1.79%	0.454µs	1	200
trace_buffer/1_senders/no_delay	throughput	2111283.001op/s	2521235.550op/s ± 40957.793op/s	2522899.695op/s ± 14679.705op/s	2540571.285op/s	2562940.067op/s	2583148.109op/s	2592414.315op/s	2.76%	-5.342	49.174	1.62%	2896.153op/s	1	200
trace_buffer/2_senders/10us_delay	execution_time	60.018ms	60.128ms ± 0.042ms	60.119ms ± 0.020ms	60.144ms	60.224ms	60.254ms	60.298ms	0.30%	1.414	2.546	0.07%	0.003ms	1	200
trace_buffer/2_senders/10us_delay	throughput	29851.915op/s	29936.360op/s ± 20.721op/s	29940.801op/s ± 9.916op/s	29950.094op/s	29959.701op/s	29963.493op/s	29990.912op/s	0.17%	-1.409	2.531	0.07%	1.465op/s	1	200
trace_buffer/2_senders/1us_delay	execution_time	51.023ms	51.247ms ± 0.072ms	51.229ms ± 0.046ms	51.285ms	51.396ms	51.425ms	51.462ms	0.45%	0.473	0.361	0.14%	0.005ms	1	200
trace_buffer/2_senders/1us_delay	throughput	34977.432op/s	35124.360op/s ± 49.629op/s	35136.203op/s ± 31.324op/s	35154.790op/s	35188.323op/s	35228.234op/s	35277.927op/s	0.40%	-0.464	0.357	0.14%	3.509op/s	1	200
trace_buffer/2_senders/no_delay	execution_time	1.588ms	1.717ms ± 0.042ms	1.714ms ± 0.027ms	1.739ms	1.791ms	1.819ms	1.847ms	7.72%	0.337	0.077	2.46%	0.003ms	1	200
trace_buffer/2_senders/no_delay	throughput	974743.270op/s	1048991.924op/s ± 25652.044op/s	1050003.448op/s ± 16454.026op/s	1068346.672op/s	1088980.918op/s	1092986.314op/s	1133784.316op/s	7.98%	-0.191	0.048	2.44%	1813.873op/s	1	200
trace_buffer/4_senders/10us_delay	execution_time	60.144ms	60.208ms ± 0.056ms	60.192ms ± 0.028ms	60.234ms	60.311ms	60.362ms	60.514ms	0.53%	1.730	4.385	0.09%	0.004ms	1	200
trace_buffer/4_senders/10us_delay	throughput	59490.528op/s	59792.396op/s ± 55.634op/s	59808.581op/s ± 27.621op/s	59832.208op/s	59848.131op/s	59854.338op/s	59856.250op/s	0.08%	-1.720	4.323	0.09%	3.934op/s	1	200
trace_buffer/4_senders/1us_delay	execution_time	51.225ms	51.447ms ± 0.129ms	51.420ms ± 0.061ms	51.493ms	51.721ms	51.811ms	51.940ms	1.01%	1.280	1.554	0.25%	0.009ms	1	200
trace_buffer/4_senders/1us_delay	throughput	69310.346op/s	69975.297op/s ± 175.286op/s	70012.134op/s ± 82.890op/s	70089.083op/s	70177.224op/s	70227.582op/s	70277.665op/s	0.38%	-1.266	1.508	0.25%	12.395op/s	1	200
trace_buffer/4_senders/no_delay	execution_time	3.626ms	3.704ms ± 0.027ms	3.705ms ± 0.015ms	3.721ms	3.745ms	3.767ms	3.774ms	1.85%	-0.245	0.251	0.74%	0.002ms	1	200
trace_buffer/4_senders/no_delay	throughput	953929.801op/s	972079.626op/s ± 7217.402op/s	971540.424op/s ± 4033.008op/s	975613.524op/s	985148.742op/s	990389.655op/s	992835.752op/s	2.19%	0.294	0.277	0.74%	510.347op/s	1	200
trace_buffer/8_senders/10us_delay	execution_time	60.246ms	60.328ms ± 0.068ms	60.309ms ± 0.028ms	60.348ms	60.444ms	60.567ms	60.767ms	0.76%	2.572	10.512	0.11%	0.005ms	1	200
trace_buffer/8_senders/10us_delay	throughput	118485.329op/s	119346.897op/s ± 133.537op/s	119385.912op/s ± 56.258op/s	119435.540op/s	119466.262op/s	119496.568op/s	119509.166op/s	0.10%	-2.552	10.349	0.11%	9.442op/s	1	200
trace_buffer/8_senders/1us_delay	execution_time	51.724ms	52.025ms ± 0.175ms	51.986ms ± 0.105ms	52.129ms	52.372ms	52.449ms	52.538ms	1.06%	0.720	-0.045	0.34%	0.012ms	1	200
trace_buffer/8_senders/1us_delay	throughput	137043.006op/s	138397.214op/s ± 465.358op/s	138497.699op/s ± 280.925op/s	138750.659op/s	139047.272op/s	139150.858op/s	139199.357op/s	0.51%	-0.706	-0.070	0.34%	32.906op/s	1	200
trace_buffer/8_senders/no_delay	execution_time	7.040ms	7.164ms ± 0.053ms	7.160ms ± 0.035ms	7.196ms	7.254ms	7.303ms	7.346ms	2.60%	0.519	0.684	0.74%	0.004ms	1	200
trace_buffer/8_senders/no_delay	throughput	980062.489op/s	1005071.645op/s ± 7442.163op/s	1005529.411op/s ± 4953.431op/s	1009897.548op/s	1016596.045op/s	1020481.923op/s	1022743.302op/s	1.71%	-0.466	0.596	0.74%	526.240op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
trace_buffer/1_senders/10us_delay	execution_time	[60.078ms; 60.087ms] or [-0.008%; +0.008%]	None	None	None
trace_buffer/1_senders/10us_delay	throughput	[14978.385op/s; 14980.650op/s] or [-0.008%; +0.008%]	None	None	None
trace_buffer/1_senders/1us_delay	execution_time	[50.708ms; 50.770ms] or [-0.061%; +0.061%]	None	None	None
trace_buffer/1_senders/1us_delay	throughput	[17727.458op/s; 17748.985op/s] or [-0.061%; +0.061%]	None	None	None
trace_buffer/1_senders/no_delay	execution_time	[356.182µs; 357.961µs] or [-0.249%; +0.249%]	None	None	None
trace_buffer/1_senders/no_delay	throughput	[2515559.194op/s; 2526911.906op/s] or [-0.225%; +0.225%]	None	None	None
trace_buffer/2_senders/10us_delay	execution_time	[60.122ms; 60.133ms] or [-0.010%; +0.010%]	None	None	None
trace_buffer/2_senders/10us_delay	throughput	[29933.488op/s; 29939.231op/s] or [-0.010%; +0.010%]	None	None	None
trace_buffer/2_senders/1us_delay	execution_time	[51.237ms; 51.257ms] or [-0.020%; +0.020%]	None	None	None
trace_buffer/2_senders/1us_delay	throughput	[35117.482op/s; 35131.238op/s] or [-0.020%; +0.020%]	None	None	None
trace_buffer/2_senders/no_delay	execution_time	[1.711ms; 1.723ms] or [-0.341%; +0.341%]	None	None	None
trace_buffer/2_senders/no_delay	throughput	[1045436.798op/s; 1052547.051op/s] or [-0.339%; +0.339%]	None	None	None
trace_buffer/4_senders/10us_delay	execution_time	[60.201ms; 60.216ms] or [-0.013%; +0.013%]	None	None	None
trace_buffer/4_senders/10us_delay	throughput	[59784.685op/s; 59800.106op/s] or [-0.013%; +0.013%]	None	None	None
trace_buffer/4_senders/1us_delay	execution_time	[51.429ms; 51.465ms] or [-0.035%; +0.035%]	None	None	None
trace_buffer/4_senders/1us_delay	throughput	[69951.004op/s; 69999.590op/s] or [-0.035%; +0.035%]	None	None	None
trace_buffer/4_senders/no_delay	execution_time	[3.700ms; 3.707ms] or [-0.103%; +0.103%]	None	None	None
trace_buffer/4_senders/no_delay	throughput	[971079.363op/s; 973079.888op/s] or [-0.103%; +0.103%]	None	None	None
trace_buffer/8_senders/10us_delay	execution_time	[60.319ms; 60.338ms] or [-0.016%; +0.016%]	None	None	None
trace_buffer/8_senders/10us_delay	throughput	[119328.390op/s; 119365.404op/s] or [-0.016%; +0.016%]	None	None	None
trace_buffer/8_senders/1us_delay	execution_time	[52.000ms; 52.049ms] or [-0.047%; +0.047%]	None	None	None
trace_buffer/8_senders/1us_delay	throughput	[138332.720op/s; 138461.708op/s] or [-0.047%; +0.047%]	None	None	None
trace_buffer/8_senders/no_delay	execution_time	[7.157ms; 7.171ms] or [-0.103%; +0.103%]	None	None	None
trace_buffer/8_senders/no_delay	throughput	[1004040.233op/s; 1006103.057op/s] or [-0.103%; +0.103%]	None	None	None

Group 1

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
write only interface	execution_time	2.058µs	2.078µs ± 0.011µs	2.074µs ± 0.005µs	2.085µs	2.099µs	2.102µs	2.104µs	1.43%	0.820	-0.456	0.52%	0.001µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
write only interface	execution_time	[2.076µs; 2.079µs] or [-0.072%; +0.072%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
redis/obfuscate_redis_string	execution_time	29.433µs	30.123µs ± 0.883µs	29.632µs ± 0.120µs	31.079µs	32.074µs	32.385µs	33.299µs	12.38%	1.253	0.344	2.92%	0.062µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
redis/obfuscate_redis_string	execution_time	[30.001µs; 30.245µs] or [-0.406%; +0.406%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
sql/obfuscate_sql_string	execution_time	293.323µs	294.206µs ± 0.453µs	294.158µs ± 0.182µs	294.323µs	294.874µs	295.647µs	297.528µs	1.15%	3.069	16.802	0.15%	0.032µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sql/obfuscate_sql_string	execution_time	[294.144µs; 294.269µs] or [-0.021%; +0.021%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
tags/replace_trace_tags	execution_time	2.610µs	2.643µs ± 0.023µs	2.639µs ± 0.011µs	2.649µs	2.681µs	2.724µs	2.749µs	4.16%	1.879	4.592	0.89%	0.002µs	1	200
tags/replace_trace_tags_v04	execution_time	2.596µs	2.780µs ± 0.064µs	2.802µs ± 0.015µs	2.818µs	2.844µs	2.851µs	2.859µs	2.03%	-1.467	1.027	2.31%	0.005µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
tags/replace_trace_tags	execution_time	[2.639µs; 2.646µs] or [-0.123%; +0.123%]	None	None	None
tags/replace_trace_tags_v04	execution_time	[2.771µs; 2.789µs] or [-0.321%; +0.321%]	None	None	None

Group 5

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
two way interface	execution_time	20.850µs	21.458µs ± 0.414µs	21.371µs ± 0.154µs	21.507µs	22.310µs	23.068µs	23.336µs	9.20%	2.024	4.943	1.92%	0.029µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
two way interface	execution_time	[21.400µs; 21.515µs] or [-0.267%; +0.267%]	None	None	None

Group 6

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
ip_address/quantize_peer_ip_address_benchmark	execution_time	4.933µs	5.029µs ± 0.042µs	5.027µs ± 0.030µs	5.050µs	5.103µs	5.108µs	5.132µs	2.09%	0.183	-0.488	0.83%	0.003µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
ip_address/quantize_peer_ip_address_benchmark	execution_time	[5.023µs; 5.035µs] or [-0.116%; +0.116%]	None	None	None

Group 7

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8175M CPU @ 2.50GHz	`36c57ed`	1788288568	andrew.glaude/noCrash

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
credit_card/is_card_number/	execution_time	3.899µs	3.923µs ± 0.004µs	3.922µs ± 0.003µs	3.926µs	3.929µs	3.931µs	3.933µs	0.29%	-0.501	4.481	0.10%	0.000µs	1	200
credit_card/is_card_number/	throughput	254233384.652op/s	254925045.265op/s ± 262645.890op/s	254964152.043op/s ± 174748.204op/s	255112849.179op/s	255234853.074op/s	255307757.931op/s	256457241.337op/s	0.59%	0.520	4.592	0.10%	18571.869op/s	1	200
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	79.973µs	80.546µs ± 0.123µs	80.537µs ± 0.068µs	80.618µs	80.727µs	80.794µs	81.057µs	0.65%	-0.482	3.845	0.15%	0.009µs	1	200
credit_card/is_card_number/ 3782-8224-6310-005	throughput	12336932.888op/s	12415230.207op/s ± 18939.916op/s	12416585.997op/s ± 10531.445op/s	12425723.684op/s	12446969.197op/s	12467918.697op/s	12504245.191op/s	0.71%	0.508	3.885	0.15%	1339.254op/s	1	200
credit_card/is_card_number/ 378282246310005	execution_time	68.318µs	68.423µs ± 0.092µs	68.401µs ± 0.049µs	68.462µs	68.544µs	68.681µs	69.234µs	1.22%	3.882	29.149	0.13%	0.006µs	1	200
credit_card/is_card_number/ 378282246310005	throughput	14443855.554op/s	14614930.004op/s ± 19502.511op/s	14619638.770op/s ± 10485.562op/s	14628305.942op/s	14633422.537op/s	14636764.144op/s	14637503.714op/s	0.12%	-3.818	28.380	0.13%	1379.036op/s	1	200
credit_card/is_card_number/37828224631	execution_time	3.884µs	3.922µs ± 0.005µs	3.921µs ± 0.003µs	3.925µs	3.930µs	3.932µs	3.935µs	0.36%	-1.637	14.058	0.13%	0.000µs	1	200
credit_card/is_card_number/37828224631	throughput	254126161.780op/s	254960637.226op/s ± 333142.196op/s	255036699.867op/s ± 200750.489op/s	255189407.960op/s	255308248.968op/s	255342927.083op/s	257468583.027op/s	0.95%	1.690	14.516	0.13%	23556.711op/s	1	200
credit_card/is_card_number/378282246310005	execution_time	64.936µs	65.043µs ± 0.067µs	65.030µs ± 0.048µs	65.085µs	65.170µs	65.223µs	65.271µs	0.37%	0.756	0.201	0.10%	0.005µs	1	200
credit_card/is_card_number/378282246310005	throughput	15320753.403op/s	15374537.693op/s ± 15899.605op/s	15377488.894op/s ± 11325.073op/s	15387597.597op/s	15394247.401op/s	15398497.846op/s	15399719.725op/s	0.14%	-0.751	0.187	0.10%	1124.272op/s	1	200
credit_card/is_card_number/37828224631000521389798	execution_time	45.568µs	45.764µs ± 0.088µs	45.764µs ± 0.060µs	45.825µs	45.913µs	45.958µs	46.032µs	0.59%	0.156	-0.329	0.19%	0.006µs	1	200
credit_card/is_card_number/37828224631000521389798	throughput	21723796.117op/s	21851269.307op/s ± 42012.497op/s	21851213.888op/s ± 28688.329op/s	21878876.504op/s	21918772.772op/s	21934369.974op/s	21945237.913op/s	0.43%	-0.147	-0.336	0.19%	2970.732op/s	1	200
credit_card/is_card_number/x371413321323331	execution_time	6.843µs	6.853µs ± 0.007µs	6.852µs ± 0.005µs	6.857µs	6.864µs	6.875µs	6.883µs	0.46%	1.168	1.966	0.10%	0.001µs	1	200
credit_card/is_card_number/x371413321323331	throughput	145278228.704op/s	145925851.604op/s ± 151810.528op/s	145951923.452op/s ± 101682.479op/s	146048268.953op/s	146103131.279op/s	146127749.994op/s	146133580.706op/s	0.12%	-1.160	1.929	0.10%	10734.625op/s	1	200
credit_card/is_card_number_no_luhn/	execution_time	3.899µs	3.923µs ± 0.005µs	3.921µs ± 0.003µs	3.926µs	3.930µs	3.933µs	3.934µs	0.33%	-0.252	2.590	0.12%	0.000µs	1	200
credit_card/is_card_number_no_luhn/	throughput	254175396.779op/s	254927270.958op/s ± 294677.364op/s	255021862.199op/s ± 187837.763op/s	255154152.673op/s	255243698.405op/s	255343632.361op/s	256492097.749op/s	0.58%	0.268	2.669	0.12%	20836.836op/s	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	65.579µs	65.828µs ± 0.075µs	65.813µs ± 0.046µs	65.879µs	65.956µs	66.039µs	66.089µs	0.42%	0.282	1.279	0.11%	0.005µs	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	15131094.509op/s	15191228.327op/s ± 17283.003op/s	15194646.928op/s ± 10683.310op/s	15202581.778op/s	15213471.529op/s	15243442.595op/s	15248897.113op/s	0.36%	-0.271	1.280	0.11%	1222.093op/s	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	53.472µs	53.576µs ± 0.060µs	53.562µs ± 0.037µs	53.613µs	53.680µs	53.777µs	53.799µs	0.44%	1.008	1.088	0.11%	0.004µs	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	18587696.853op/s	18664992.687op/s ± 21027.007op/s	18670019.683op/s ± 12877.442op/s	18680073.032op/s	18691681.828op/s	18696670.871op/s	18701245.503op/s	0.17%	-1.001	1.064	0.11%	1486.834op/s	1	200
credit_card/is_card_number_no_luhn/37828224631	execution_time	3.888µs	3.923µs ± 0.005µs	3.922µs ± 0.003µs	3.926µs	3.931µs	3.935µs	3.938µs	0.39%	-1.485	13.798	0.12%	0.000µs	1	200
credit_card/is_card_number_no_luhn/37828224631	throughput	253958592.002op/s	254880851.672op/s ± 315945.928op/s	254945314.233op/s ± 183653.475op/s	255071368.970op/s	255249304.551op/s	255275190.997op/s	257231067.945op/s	0.90%	1.536	14.207	0.12%	22340.751op/s	1	200
credit_card/is_card_number_no_luhn/378282246310005	execution_time	50.243µs	50.344µs ± 0.061µs	50.335µs ± 0.042µs	50.379µs	50.450µs	50.538µs	50.578µs	0.48%	1.010	1.260	0.12%	0.004µs	1	200
credit_card/is_card_number_no_luhn/378282246310005	throughput	19771613.031op/s	19863217.742op/s ± 23880.788op/s	19866783.731op/s ± 16478.580op/s	19882260.054op/s	19892042.686op/s	19897895.561op/s	19903090.950op/s	0.18%	-1.002	1.230	0.12%	1688.627op/s	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	45.465µs	45.795µs ± 0.130µs	45.821µs ± 0.079µs	45.887µs	45.963µs	46.043µs	46.106µs	0.62%	-0.545	-0.214	0.28%	0.009µs	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	21689155.383op/s	21836792.267op/s ± 61937.040op/s	21823990.327op/s ± 37689.760op/s	21884522.187op/s	21959981.625op/s	21993122.157op/s	21995003.017op/s	0.78%	0.557	-0.203	0.28%	4379.610op/s	1	200
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	6.842µs	6.853µs ± 0.007µs	6.852µs ± 0.005µs	6.857µs	6.865µs	6.872µs	6.876µs	0.36%	0.804	0.437	0.10%	0.000µs	1	200
credit_card/is_card_number_no_luhn/x371413321323331	throughput	145432754.086op/s	145931005.631op/s ± 144025.090op/s	145950588.505op/s ± 108675.041op/s	146057152.165op/s	146102096.876op/s	146150762.111op/s	146153958.580op/s	0.14%	-0.799	0.421	0.10%	10184.112op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
credit_card/is_card_number/	execution_time	[3.922µs; 3.923µs] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number/	throughput	[254888645.071op/s; 254961445.459op/s] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	[80.529µs; 80.563µs] or [-0.021%; +0.021%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	throughput	[12412605.317op/s; 12417855.097op/s] or [-0.021%; +0.021%]	None	None	None
credit_card/is_card_number/ 378282246310005	execution_time	[68.411µs; 68.436µs] or [-0.019%; +0.019%]	None	None	None
credit_card/is_card_number/ 378282246310005	throughput	[14612227.143op/s; 14617632.864op/s] or [-0.018%; +0.018%]	None	None	None
credit_card/is_card_number/37828224631	execution_time	[3.921µs; 3.923µs] or [-0.018%; +0.018%]	None	None	None
credit_card/is_card_number/37828224631	throughput	[254914466.921op/s; 255006807.530op/s] or [-0.018%; +0.018%]	None	None	None
credit_card/is_card_number/378282246310005	execution_time	[65.033µs; 65.052µs] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number/378282246310005	throughput	[15372334.161op/s; 15376741.226op/s] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number/37828224631000521389798	execution_time	[45.752µs; 45.776µs] or [-0.027%; +0.027%]	None	None	None
credit_card/is_card_number/37828224631000521389798	throughput	[21845446.779op/s; 21857091.835op/s] or [-0.027%; +0.027%]	None	None	None
credit_card/is_card_number/x371413321323331	execution_time	[6.852µs; 6.854µs] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number/x371413321323331	throughput	[145904812.125op/s; 145946891.083op/s] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number_no_luhn/	execution_time	[3.922µs; 3.923µs] or [-0.016%; +0.016%]	None	None	None
credit_card/is_card_number_no_luhn/	throughput	[254886431.509op/s; 254968110.407op/s] or [-0.016%; +0.016%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	[65.817µs; 65.838µs] or [-0.016%; +0.016%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	[15188833.069op/s; 15193623.585op/s] or [-0.016%; +0.016%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	[53.568µs; 53.585µs] or [-0.016%; +0.016%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	[18662078.546op/s; 18667906.828op/s] or [-0.016%; +0.016%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	execution_time	[3.923µs; 3.924µs] or [-0.017%; +0.017%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	throughput	[254837064.605op/s; 254924638.739op/s] or [-0.017%; +0.017%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	execution_time	[50.336µs; 50.353µs] or [-0.017%; +0.017%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	throughput	[19859908.095op/s; 19866527.390op/s] or [-0.017%; +0.017%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	[45.777µs; 45.813µs] or [-0.039%; +0.039%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	[21828208.389op/s; 21845376.145op/s] or [-0.039%; +0.039%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	[6.852µs; 6.853µs] or [-0.014%; +0.014%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	throughput	[145911045.138op/s; 145950966.123op/s] or [-0.014%; +0.014%]	None	None	None

Baseline

Omitted due to size.

dd-octo-sts · 2026-09-01T15:42:24Z

Artifact Size Benchmark Report

aarch64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a	90.77 MB	90.78 MB	+.01% (+10.78 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so	8.39 MB	8.39 MB	0% (0 B) 👌

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	101.99 MB	102.00 MB	+.01% (+10.83 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.29 MB	11.29 MB	+0% (+352 B) 👌

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	27.03 MB	27.03 MB	+.01% (+5.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	96.08 KB	96.08 KB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	181.69 MB	181.71 MB	+0% (+16.00 KB) 👌
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	766.69 MB	766.69 MB	-0% (-324 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	8.91 MB	8.92 MB	+.02% (+2.50 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	96.08 KB	96.08 KB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	26.05 MB	26.05 MB	+.02% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	51.84 MB	51.85 MB	+.01% (+8.78 KB) 🔍

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	23.57 MB	23.58 MB	+.03% (+8.00 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	97.58 KB	97.58 KB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	186.76 MB	186.77 MB	+0% (+8.00 KB) 👌
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	754.57 MB	754.53 MB	-0% (-38.83 KB) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	6.88 MB	6.89 MB	+.01% (+1.00 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	97.58 KB	97.58 KB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	28.00 MB	28.00 MB	0% (0 B) 👌
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	49.34 MB	49.34 MB	+.01% (+6.83 KB) 🔍

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	80.93 MB	80.94 MB	+0% (+8.17 KB) 👌
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	9.33 MB	9.33 MB	0% (0 B) 👌

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	96.69 MB	96.70 MB	+0% (+8.12 KB) 👌
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	11.37 MB	11.37 MB	+0% (+304 B) 👌

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yannham · 2026-09-01T17:10:21Z

                                    let close_tag = &self.s[inner_end..outer_end];
-                                    let obfuscated_inner =
-                                        obfuscate_sql(inner, self.config, self.dbms);
+                                    if self.depth + 1 >= MAX_DOLLAR_QUOTE_DEPTH {


Very nit, but since the constant is arbitrary to begin with, the + 1 looks a bit superfluous. We could decrement MAX_DOLLAR_QUOTE_DEPTH if needed.

Suggested change

if self.depth + 1 >= MAX_DOLLAR_QUOTE_DEPTH {

if self.depth >= MAX_DOLLAR_QUOTE_DEPTH {

I think we actually need this since otherwise we create a gap where we're off by 1 (if you try removing this you can see this happens with failing the test even if you lower the constant by 1).

…view nits Bound strip_userinfo_best_effort's authority scan to path_end instead of path_query_end in the control-char fallback, matching the parse-error fallback, so an '@' inside the query is never mistaken for userinfo. Also renames the tokenizer's dollar-quote recursion depth field for clarity. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

github-actions Bot added the mini-agent label Sep 1, 2026

ajgajg1134 marked this pull request as ready for review September 1, 2026 17:03

ajgajg1134 requested review from a team as code owners September 1, 2026 17:03

chatgpt-codex-connector Bot reviewed Sep 1, 2026

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Comment thread libdd-trace-obfuscation/src/http.rs Outdated

yannham approved these changes Sep 1, 2026

View reviewed changes

	if self.depth + 1 >= MAX_DOLLAR_QUOTE_DEPTH {
	if self.depth >= MAX_DOLLAR_QUOTE_DEPTH {

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ajgajg1134 commented Sep 1, 2026

Summary

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📚 Documentation Check Results

📦 libdd-trace-obfuscation - 853 warning(s)

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🔒 Cargo Deny Results

📦 libdd-trace-obfuscation - 3 error(s)

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scenario:redis/obfuscate_redis_string

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github-actions Bot commented Sep 1, 2026 •

edited

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📦 `libdd-trace-obfuscation` - 853 warning(s)

github-actions Bot commented Sep 1, 2026 •

edited

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📦 `libdd-trace-obfuscation` - 3 error(s)

datadog-datadog-prod-us1-2 Bot commented Sep 1, 2026 •

edited by datadog-datadog-prod-us1 Bot

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