Threat guide
LLM vulnerabilities enterprise buyers need to control before AI scales.
LLM vulnerabilities are not limited to model behavior. They appear wherever prompts, retrieved context, tools, provider routes, and sensitive data meet. PromptWall reduces these risks through an LLM security platform designed for enterprise operating controls.
Control
Policy first
Map every AI interaction to allow, flag, mask, or block decisions.
Data
DLP aware
Detect sensitive prompts, regulated data, and document leakage risk.
Evidence
Audit ready
Keep explainable records for security, risk, and compliance reviews.
Risk map
The highest-value LLM vulnerabilities happen across the interaction chain.
Security teams should evaluate direct prompt injection, indirect prompt injection through retrieved content, sensitive prompt leakage, credential exposure, unsafe tool invocation, excessive data access, weak auditability, and provider sprawl. The buying trigger is rarely one isolated vulnerability; it is the lack of a durable control layer.
Prompt risk
Prompt injection attack patterns
Understand direct, indirect, encoded, and multi-step prompt attack paths.
Read more
Data risk
AI data leakage cases
Map sensitive prompt leakage to business and compliance impact.
Read more
RAG risk
RAG security design
Treat retrieved content as untrusted context with its own security controls.
Read more
Control model
PromptWall converts vulnerability classes into enforceable policy.
Instead of publishing vulnerability lists without operational action, PromptWall maps each threat class to allow, flag, mask, or block outcomes. That makes the content useful for CISOs and platform owners who need controls, not only awareness.
Turn LLM vulnerability awareness into controls
Use PromptWall to map prompt, data, gateway, and governance risks into an enforceable rollout plan.
Frequently asked questions
What are common LLM vulnerabilities?+
Common LLM vulnerabilities include prompt injection, sensitive data leakage, insecure retrieval, unsafe tool use, weak provider governance, and insufficient auditability.
Can LLM vulnerabilities be solved only with model tuning?+
No. Model tuning can reduce some behavior risk, but enterprise security also needs controls around prompts, data, routing, identity, and audit.