bug-bounty497
google347
xss301
microsoft290
facebook261
rce211
exploit198
malware168
apple161
cve135
account-takeover115
bragging-post102
privilege-escalation96
csrf90
phishing86
browser75
writeup74
authentication-bypass69
supply-chain67
dos66
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react52
input-validation49
cross-site-scripting48
cloudflare47
aws47
docker46
web-security46
lfi46
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sql-injection45
web-application44
ethereum44
ctf43
web343
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oauth43
node41
race-condition39
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open-source39
idor37
cloud37
info-disclosure36
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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