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The OWASP Top 10 for LLM Applications, when placed against the path data takes through an application, becomes a set of named positions on a single pipeline spanning input, retrieval, model, tools, output, and an overlapping supply chain segment.
The author maps each OWASP Top 10 LLM risk onto a stage of the data pipeline — including examples such as PoisonedRAG corrupting a RAG system with as few as five malicious passages at 90 percent success — and notes supply chain spans every stage while attention mismatches production reality. ✦ AI generated
Article author (GLM pipeline) · ByteByteGo Newsletter · 2026-08-03 · original ↗
The OWASP Top 10 for Large Language Model Applications is the industry reference for the most critical LLM risks. Its recent edition presents ten separate items. Placed against the path that data takes through an application, those ten items become positions on a single map. The pipeline runs in stages. Input arrives from the user. ... Retrieval: Indirect injection, vector, and embedding weaknesses. PoisonedRAG, a 2024 study, corrupted a RAG system's answers by inserting as few as five malicious passages into a knowledge base of millions, reaching a 90 percent success rate on targeted questions. The map also exposes a mismatch in attention. The threats that generate the most concern and the threats that reach production are different.
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explains mechanism → The point where LLM attacks cause material damage is the lethal trifecta: an agent holding access to private data, exposure to untrusted content, and a channel to act externally can be directed by injected instructions to exfiltrate private data, and removing any one capability reduces exposure.Article author (GLM pipeline) · ByteByteGo Newsletterprovides context → Prompt injection reaches a model by two routes — a direct route (hostile instructions typed into the chat box) and an indirect route (instructions embedded in content the model retrieves during a legitimate task) — and the EchoLeak incident demonstrates the indirect route passing through a dedicated cross-prompt-injection classifier.Article author (GLM pipeline) · ByteByteGo Newsletterprovides context → Supply chain attacks, ranging from a compromised model to a poisoned vector store, bypass runtime defenses because the threat is present before input validation runs, yet provenance is the surface most directly controllable and the attack source spanning all stages.Article author (GLM pipeline) · ByteByteGo Newsletterprovides context → Almost every LLM vulnerability traces to a single property: a language model receives instructions and data as one concatenated sequence of tokens with no marker separating commands from information, which is the root cause of the entire threat model.Article author (GLM pipeline) · ByteByteGo Newsletterprovides context → Attacks aimed at a model's interior — model theft, training-data extraction, and poisoning — are bounded and largely mitigated for most developers, ranking low for initial effort compared to risks around external actions.Article author (GLM pipeline) · ByteByteGo Newsletter