ieeesp-reproducibility
Use when hardening the reproducibility of an IEEE S&P (Oakland) paper's evidence before submission, including environment pinning for exploits and s…
它会碰到什么
这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。
技能内容
IEEE S&P Reproducibility
Use this while the evidence is being produced — months before
ieeesp-artifact-evaluation packages it. Security results decay in ways ML
results do not: targets get patched, infrastructure changes, and an attack
that "worked in March" can be unreproducible by review time. Reproducibility
at S&P is first about freezing the world you measured.
Freeze the target, not just the code
| Evidence type | What must be pinned | Decay risk if not |
|---|---|---|
| Software exploit | Target version, build flags, distro, patch level | Silent fix ships mid-review |
| Microarchitectural attack | CPU model + stepping, microcode, OS mitigations state | Microcode update changes timing |
| Network measurement | Scan dates, vantage points, target-list snapshot | Internet moved; sample unrecoverable |
| Web/API study | Crawl date, client fingerprint, geographic origin | Server-side behavior shifts |
| ML attack/defense | Model weights hash, dataset version, threat-budget ε | "Same" model retrained differently |
Record these in the paper, not only in lab notes — a reviewer asking "does
this still work after <vendor>'s April update?" is a reproducibility question
about the world, and the answer is a documented snapshot date.
Determinism ledger for probabilistic evidence
Attacks with success probabilities, fuzzing campaigns, and randomized
defenses need trial discipline:
- Report trials, not anecdotes: success rate over n runs with n stated,
plus dispersion — a "9/10 successful" exploit and a "measured once" exploit
are different claims.
- Fuzzing comparisons follow the field's known pitfalls: equal CPU-time
budgets, multiple campaigns, identical seed corpora — state all three.
- Randomized defenses (ASLR-like, moving-target): evaluate across the
randomness, not one lucky layout.
- Timing measurements: report the noise floor and the machine's quiescence
conditions (isolated cores, frequency pinning) or the numbers will not
transfer.
Reproducibility ledger (one row per experiment in the paper):
exp_id | claim it supports | target snapshot (ver/date/hw) |
trials & seeds | dispersion reported? | rerun cost (time/hw/$) |
rerunnable by outsider? (yes / gated / world-dependent)
The last column becomes the availability statement and the honest badge
target later.
When ethics limits release, say exactly what and why
S&P reviewers accept withheld material when the reasoning is specific:
- "Exploit for CVE-pending issue withheld until the fix ships; released to
reviewers via the chairs on request" beats a silent gap.
- User-derived datasets: describe schema and collection so others can
re-collect ethically, even when raw data cannot ship.
- Never fabricate openness — a promised-but-empty repository found during
review or after publication is a reputation event, and at this venue
reviewers do check.
Cheap habits that pay at rebuttal time
- One
env.lockper experiment directory: container digest, package list,
kernel and microcode versions, dumped automatically by the run script.
- Raw outputs archived before aggregation; the rebuttal question is always
about a number two steps upstream of the figure.
- A
regenerate_figures.shthat goes from archived raw data to every figure
— this is also the artifact-evaluation core later.
- Date-stamped disclosure and measurement logs, because ethics questions in
review are answered with timelines (ieeesp-author-response).
What this venue does not require
Keep effort calibrated: S&P has no submission-time reproducibility checklist
in the verified 2026/2027 materials (待核实 each cycle), artifact evaluation
is post-acceptance and optional, and appendices are explicitly not
guaranteed reader attention. The reproducibility work above is therefore
aimed at three audiences in order: your own rebuttal, the shepherd, and the
AE committee — not at a submission-form requirement.
Output format
[Ledger status] <n>/<total> experiments with snapshot + trials + dispersion
[World-dependence] <which results cannot be re-run by anyone, ever — flagged in text?>
[Release plan] open / gated (reason) / withheld (reason) — per component
[Rebuttal readiness] raw data archived ✓/✗ · env locks ✓/✗ · figure regen ✓/✗
[Gaps to close before registration week] <ordered list>想直接用这个技能?
本站把开放许可(MIT / Apache 等)的技能按仓库打包整理到网盘,点一下转存到你自己的网盘,不用一个个从 GitHub 拉。许可未声明的技能只给原始仓库链接,不打包。
它属于哪个仓库
IEEE-SP-Skills/skills/ieeesp-reproducibility/SKILL.md