Own the definitional queries LLM/agent teams ask before they ship customer-facing AI that accepts untrusted input.
Definitional · FAQPage
What is prompt injection? (LLM01 in the OWASP Top 10 for LLM Apps)
Target query: what is prompt injection
Prompt injection is an attack where untrusted user input hijacks a model's instructions — making it ignore its system prompt and do something unintended, like leaking data, calling a tool it shouldn't, or exfiltrating context. OWASP lists it as LL01 in the Top 10 for Large Language Model Applications. PromptShield scans your app's input for the exact patterns that fire.
refs: https://owasp.org/www-project-top-10-for-large-language-model-applications/ · https://arxiv.org/abs/2306.05499
Definitional + examples
Why LLM apps are injection-prone (and the real failure modes)
Target query: why are llm apps vulnerable to prompt injection
An LLM app blends trusted instructions (your system prompt) with untrusted input (the user) in the same context window. The model can't reliably tell them apart. The failure modes are concrete: instruction override, system-prompt exfiltration, tool abuse, and data theft. PromptShield flags the patterns before they reach production.
refs: https://owasp.org/www-project-top-10-for-large-language-model-applications/ · https://arxiv.org/abs/2306.05499
How-to · HowTo
How to scan your AI app for prompt injection before shipping
Target query: how to prevent prompt injection
Describe your AI app and paste a sample of untrusted user input; PromptShield returns an injection-risk report with the exact patterns that fired and a hardening checklist. For continuous coverage, run it on every deploy as a CI gate (Pro). Scanning before ship turns an invisible risk into a visible, fixable one.
refs: https://owasp.org/www-project-top-10-for-large-language-model-applications/ · https://owasp.org/
Definitional + examples
Prompt injection vs jailbreak: what's the difference?
Target query: prompt injection vs jailbreak
Injection arrives through untrusted input at runtime — a user message that hijacks the app. A jailbreak is a deliberate attempt to bypass the model's safety behavior ("you are now DAN"). Both are adversarial; PromptShield scans for injection patterns in your app's real input, and flags jailbreak signals too.
refs: https://owasp.org/www-project-top-10-for-large-language-model-applications/ · https://arxiv.org/abs/2306.05499
How-to · HowTo
LLM app hardening checklist (pre-ship)
Target query: llm security hardening checklist
Before you ship a customer-facing LLM: validate/escape user input at the boundary, keep the system prompt out of model-returnable context, add an injection classifier before tool calls, and default-deny tool access. PromptShield's report gives you this checklist per scan.
refs: https://owasp.org/www-project-top-10-for-large-language-model-applications/ · https://owasp.org/
Long-form, cited explainers. Each carries OWASP LLM Top 10 (2025) + prompt-injection research references and an honesty disclaimer.
A short walkthrough of PromptShield — describe your AI app, paste a sample of untrusted input, and get an injection-risk report with the patterns that fired and a hardening checklist. No model changes, no security PhD required.
A practical walkthrough: describe your AI app, paste a sample of untrusted input, read the injection-risk report, and turn it into a repeatable CI gate. Scanning before ship turns an invisible risk into a visible, fixable one.
A pre-ship checklist for customer-facing LLM apps: validate input at the boundary, separate the system prompt from returnable context, gate tool calls, filter output, and scan before every deploy. PromptShield's report gives you this checklist per scan.
The OWASP Top 10 for LLM Applications is a risk catalog, not a compliance certificate. Prompt injection is LLM01. Adjacent categories — insecure output handling, training-data poisoning, supply-chain, and excessive agency — are where injection often turns into real damage.
Prompt injection isn't one trick — it's a set of repeatable patterns. Direct instruction override, system-prompt exfiltration, tool/function abuse, delimiter confusion, and indirect injection through retrieved content. Knowing the shapes helps you spot them before an attacker does.
Injection arrives through untrusted input at runtime and hijacks your app; a jailbreak is a deliberate attempt to bypass the model's safety behavior. Both are adversarial, both matter — but they enter through different doors and need different defenses.
Prompt injection is an attack where untrusted user input hijacks a model's instructions — making it ignore its system prompt and act against your intent. OWASP lists it as LLM01, the #1 risk for LLM applications. Here is what it is, why it tops the list, and how a scanner like PromptShield helps.
An LLM app blends trusted instructions with untrusted input in the same context window, and the model can't reliably tell them apart. That design choice creates the failure modes builders actually hit — instruction override, system-prompt exfiltration, tool abuse, and data theft.
Publish + syndicate per gtm-launch (IH + GEO indexes). Each post carries authoritative refs.