The vulnerability pattern
Prompt injection attacks have claimed the top position in OWASP's Top 10 for Large Language Model Applications. The timing matters: testing of DeepSeek R1 in January 2025 revealed alarming success rates for both direct and indirect attacks, suggesting security practices in model development aren't keeping pace with deployment.
The architectural problem is fundamental. LLMs process system instructions and user inputs as continuous text streams, unable to inherently distinguish between trusted developer commands and malicious user inputs. This differs from traditional code vulnerabilities where clear boundaries exist between execution logic and data.
Attack surface reality
Two primary attack vectors exist. Direct prompt injection targets system prompts through user interfaces - attackers use commands like "forget all previous instructions" or obfuscation through alternate languages to override legitimate instructions. More dangerous: indirect prompt injection embeds malicious prompts in external data sources. Users unknowingly trigger compromises by asking LLMs to summarize webpages or process uploaded documents.
Real-world patterns show sophistication. Backend exploitation enables remote code execution. Multi-step social engineering uses conditional triggers that activate only under specific circumstances. Data exfiltration occurs through injections that cause LLMs to extract private conversations via image embedding links.
Defense requirements
Effective mitigation requires layered validation: allowlists of permitted input patterns, denylists flagging common attack phrases, semantic filters detecting attempts to establish new roles, proper input sanitization before data reaches models, and granular access controls limiting information retrieval.
Major security organizations - OWASP, AWS, PortSwigger - now classify prompt injection as a critical vulnerability category equivalent to SQL injection or XSS in severity. For APAC enterprises, the implications are clear: any organization deploying customer-facing LLM applications without robust prompt injection defenses faces exposure to data theft, privilege escalation, and reputational damage.
The January 2025 DeepSeek findings suggest this risk extends across different model architectures and vendors. History suggests the vulnerability discovered in 1998 - SQL injection - still appears in production systems. We're seeing the pattern repeat.