bug-bounty489
google380
microsoft317
xss286
facebook275
rce197
exploit182
malware181
apple178
cve129
account-takeover115
bragging-post102
phishing86
csrf86
privilege-escalation85
browser74
supply-chain70
stored-xss65
dos65
authentication-bypass64
react61
writeup60
reflected-xss57
cloudflare53
reverse-engineering52
node50
ssrf49
input-validation48
cross-site-scripting48
access-control48
aws47
docker47
oauth45
smart-contract45
ethereum44
sql-injection43
web-security43
web343
defi43
lfi41
web-application41
info-disclosure40
cloud39
pentest38
race-condition37
ctf36
auth-bypass35
idor35
vulnerability-disclosure35
burp-suite35
0
5/10
This article introduces golden sets—structured regression testing frameworks for probabilistic AI workflows that combine representative test cases, explicit scoring rubrics, and versioned evaluation contracts to detect regressions across prompt, model, retrieval, and policy changes before production impact.
ai-systems
testing
regression-testing
evaluation
quality-assurance
probabilistic-systems
prompt-engineering
llm-safety
policy-enforcement
metric-design
operational-guidelines
Heavy Thought Laboratories