Agent Skills
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ieeesp-experiments
GitHub用于设计或审计IEEE S&P论文的实验评估，涵盖攻击演示、自适应防御评估、测量有效性及伦理约束。指导如何匹配贡献类型与评估方法，强调自适应对手规则、统计显著性及实验伦理，确保满足顶会评审对真实性和严谨性的要求。
Trigger Scenarios
设计IEEE S&P论文实验方案
审计安全系统评估的严谨性
构建自适应攻击者模型
制定测量研究的采样与伦理规范
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ieeesp-experiments -g -y
SKILL.md
Frontmatter
{
"name": "ieeesp-experiments",
"description": "Use when designing or auditing the evaluation of an IEEE S&P (Oakland) paper, including end-to-end attack demonstration, adaptive-adversary evaluation of defenses, measurement sampling and validity, baselines and ablations, statistical reporting of attack success, and the ethics constraints that shape what experiments are permissible."
}
IEEE S&P Experiments
Use this to build or audit the evidence an S&P paper stands on. The venue's reviewers are professional adversaries; an evaluation that would satisfy a systems or ML PC often leaves an Oakland reviewer's central question — "does this survive a real, adaptive attacker?" — unanswered.
Match the evaluation to the contribution type
| Contribution | Evaluation that closes the loop | Fatal gap |
|---|---|---|
| Attack | End-to-end demonstration against a realistic, current target | Toy target; unrealistic preconditions |
| Defense | Adaptive adversary who knows the design; cost/overhead | Only non-adaptive or prior attacks |
| Measurement | Representative sampling + validation + ethics | Convenience sample presented as population |
| System | Security property demonstrated and performance | Property asserted, not tested |
| SoK | Systematic re-analysis under one framework | Cherry-picked coverage |
The adaptive-adversary rule dominates defense papers
A defense evaluated only against existing or non-adaptive attacks is the most common S&P defense rejection. The standard:
- Define the adaptive adversary explicitly: knows the mechanism, the parameters, and the deployment.
- Show your defense against attacks designed to break it, not just yesterday's attacks it happens to stop.
- Report the cost of adaptation for the attacker and the overhead for the defender — both are part of the security claim.
- If a class of adaptive attack is out of scope, say so in the threat model and own the boundary; do not leave it for a reviewer to discover.
Measurement validity is an evidence question and an ethics question
For measurement papers, the sampling story and the ethics story are the same paragraph in reviewers' minds:
- State the population, the frame, and the sampling method; quantify coverage and bias.
- Validate a subsample by an independent method where possible.
- Active measurement (scanning, probing) must respect opt-out norms, rate
limits, and the ethics record (
ieeesp-review-process) — an experiment that harms the systems it measures is a reject regardless of results. - Human-subjects components need IRB determination before running, not a post-hoc note.
Statistics for attacks and fuzzing
Security evidence is often probabilistic and gets held to a real bar:
Attack-success reporting:
n trials (state n) · success rate ± dispersion · target set described
→ "worked" without n is an anecdote, not a result
Fuzzing / bug-finding comparison (the field's known pitfalls):
- equal budgets (CPU-time, not wall-clock)
- ≥ 5–10 campaigns per configuration; report variance
- identical seed corpora across compared tools
- a ground-truth or triage method for "unique" bugs
→ a single-run bug count comparison is not evidence of superiority
Timing / side-channel:
noise floor stated · machine quiescence (isolated cores, freq pinning)
· distinguisher's statistical test named
Baselines and ablations Oakland reviewers ask for
- The strongest prior attack/defense, at its best configuration, not a weakened reimplementation.
- An ablation isolating the component you claim is responsible for the security gain.
- A cost baseline: what does the attacker/defender spend, and is it realistic at the claimed scale?
- Negative results where they bound the claim (attack fails against target class Y — state it; it strengthens the scoped claim).
Ethics as an experimental design constraint, not an afterthought
Some experiments are simply not runnable as first imagined:
- Testing an exploit against live third-party systems without authorization is out; build a representative testbed instead.
- Collecting user data beyond what the IRB and the ethics record cover is out.
- Disclosure timing constrains when certain measurements can be published
— design the timeline so the evidence and the fix do not collide
(
ieeesp-reproducibility).
Audit worksheet
For each experiment:
claim it supports | contribution type | adaptive adversary evaluated? |
strongest baseline used? | n trials + dispersion | ethics clearance |
realistic target? | rerunnable? (→ ieeesp-reproducibility)
Flag any row with: non-adaptive-only defense · anecdotal success rate ·
weakened baseline · unmet ethics precondition
Output format
[Contribution type] attack / defense / measurement / system / SoK
[Loop closed?] <the demonstration/eval that proves the claim> ✓/✗
[Adaptive adversary] evaluated ✓/✗/n-a — scope stated?
[Baselines] strongest prior used ✓/✗ · ablation ✓/✗ · cost baseline ✓/✗
[Statistics] trials+dispersion ✓/✗ · fuzzing pitfalls avoided ✓/✗/n-a
[Ethics preconditions] IRB ✓/✗/n-a · authorization ✓/✗ · disclosure timing ok ✓/✗
Version History
- 9f86f09 Current 2026-07-19 15:59
