bug-bounty498
google355
xss301
microsoft298
facebook263
rce211
exploit200
malware171
apple164
cve136
account-takeover115
bragging-post102
privilege-escalation95
csrf90
phishing86
browser75
writeup74
authentication-bypass69
supply-chain68
dos66
stored-xss65
reflected-xss57
ssrf56
reverse-engineering55
react52
access-control51
input-validation49
cross-site-scripting48
aws47
cloudflare47
docker46
web-security46
lfi46
sql-injection45
smart-contract45
ethereum44
web-application44
web343
defi43
ctf43
oauth43
node43
pentest40
race-condition39
idor37
open-source37
cloud37
burp-suite36
info-disclosure36
auth-bypass35
0
6/10
research
The article describes a multi-model AI validation architecture for financial analysis that uses deliberate model disagreement and fact auditing to detect hallucinations and silent failures in AI outputs. The approach mitigates risks from single-model systems by implementing output validation, cascading fallbacks, and RAG-based verification across multiple independent models with conflicting prompts.
ai-security
prompt-injection
hallucination
multi-model-validation
output-validation
adversarial-ai
fact-checking
rag
ai-reliability
open-source
Nipun AI
Google Gemini
Cerebras
Llama 3.3 70B
Cohere Command R+
Finnhub
Raviteja Nekkalapu