AI Security Testing: LLM-01 Finding Prompt Injection Flaws

Explore how large language models function as input-output systems from a QA perspective, examine the trust boundary, and compare prompt injection to the classic sequel injection vulnerability.

Build a foundation for understanding ai systems by explaining what a prompt is and how system prompts and user input share a context window, revealing where prompt injection vulnerabilities arise.

Discover how llms process system prompts, user inputs, and external data, why these sources all look like text to the model, and how that expands prompt injection risk.

Explore direct injection, the most straightforward form where attackers type payloads into the user input field, the starting point for testing chat interfaces, with real-world examples and a live demo.

Explore how attackers extract the system prompt through completion, translation requests, and roleplay, exposing guardrails, business logic, and API keys while challenging the defense to hold against every prompt-injection technique.

Learn how jailbreaking targets a model's safety training and guardrails using adversarial personas and logic traps, differentiating it from direct injection for bug reporting.

Jailbreaks expose LLM attack patterns driven by prompt manipulation, not single prompts, using DAN as an early example; QA testing should target behavior patterns with variation and clear fail criteria.

Differentiate prompt injection from jailbreaking by their targets and risks, and apply the correct remediation path—prompt fixes or external safety filters.

Explores fictional framing as a contextual jailbreak that tests safety alignment, showing how content filters and intent-based defenses can miss harmful guidance wrapped in fiction.

Indirect injection shifts the attack path from prompts to processed content, with hidden instructions in documents. Quality assurance teams test every content surface to protect trust boundaries against poisoned documents.

Identify how indirect injection leverages content to trigger injected instructions, creating a landmine in documents, emails, or web pages, with a payload leading to data exfiltration.

Demonstrates image injection demo setup in a pdf-attack against an AI legal assistant using Mistral. Compare baseline confidentiality summary with a high-severity fail caused by poisoned pdf instructions bypassing guardrails.

Demonstrates an image injection attack against an invoice processing assistant. Observe how a crafted prompt can override instructions and approve payment.

Discover how RAGs grounding of LLMs exposes a hidden attack surface through asynchronous poisoning of knowledge bases, and apply QA testing and remediation with sanitization and strict retrieval filtering.

Move from manual poking to a structured, repeatable test matrix that defines a secure state for every vector and sets success criteria before tests.

Craft a precise bug report detailing endpoints and payloads to reproduce prompt injection flaws, noting expected vs actual results, attack pattern, system prompt exposure, business impact, and remediation.

Integrate prompt injection testing across the full software development lifecycle from requirements to release, using threat modeling, automated payload scans, and a reusable Python suite in ci-cd.

Implement a repeatable security workflow with automated regression against a production API and nightly payload tests to detect prompt drift, then expand with adversarial red teaming for novel bypasses.

Learn six defense strategies for secure llm apps. Map verification tests to mitigations including input validation, prompt hardening, output filtering, privileged separation, human in the loop, and content provenance.

Execute the defense verification checklist with blackbox testing across input controls, output controls, and architecture to verify boundaries, leakage prevention, logging, and least-privilege design.

Learn to identify and remediate LLM prompt injection by distinguishing direct and indirect vectors, mapping global surfaces, and using QA testing to report reproducible bugs.

Automate LLM-01 prompt injection testing by converting manual direct, jailbreaking, and indirect injections into a repeatable local workflow that runs payloads, captures responses, and flags failures.

Explore the test harness architecture for automated prompt-injection testing, using test case files with payloads, a local runner, and structured artifacts for traceable evaluations that cover cases and evaluate responses.

Ensure reproducibility in ai security testing by controlling the environment and execution protocol: fix temperature and top p, pin the model version, and run five sessions with raw outputs preserved.

Learn to generate comprehensive test evidence using run log csv, failures.json, summary.md, and raw response traces for audit, debugging, and trend analysis.

Run a downloadable exercise that loads test cases and pin configuration values and yields run logs and failure files, recognizing it as a regression exercise that requires manual testing.