AI Security Intelligence Hub
The definitive engineering blueprint for securing large language models, RAG systems, and autonomous AI agents against modern exploits.
OWASP LLM
Top 10 risks for securing LLM applications
Exploiting model alignment via direct user overrides or indirect payloads hidden in external data sources to manipulate core behavior.
Inadvertent exfiltration of proprietary data, API keys, or personally identifiable information (PII) through systemic model output leakage.
Vulnerabilities rooted in third-party base models, poisoned fine-tuning datasets, unverified plugins, or malicious package dependencies.
Malicious manipulation of pre-training data, fine-tuning distributions, or live retrieval-augmented generation (RAG) inputs to create persistent backdoors.
Blind acceptance of model-generated outputs without strict validation, enabling downstream Cross-Site Scripting (XSS), SSRF, or remote code execution.
Granting autonomous AI agents overly permissive access, excessive privileges, or destructive capabilities without human-in-the-loop validation.
Adversarial extraction of foundational configuration instructions, hidden system prompts, framing rules, and restricted operational bounds.
Exploiting semantic data pipelines, unauthenticated vector database queries, or embedding inversions to bypass access controls.
Systemic generation of mathematically confident but factually incorrect outputs, leading to security logic failures or data corruption.
Resource exhaustion vulnerabilities stemming from unchecked recursive agent loops, token flooding, or high-concurrency denial of service (DoS).
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