Large-language-model–powered applications went from hack-day demos to billable features in record time. E-commerce chatbots write refund policies, help-desk copilots triage tickets, and data-analysis agents turn CSVs into slide decks—often shipping to production after only lightweight QA. Traditional test suites were never built for systems that can be talked into misbehaving, and the gap shows. Below are the five failure modes teams encounter most often, along with practical ways to probe for each one before customers (or criminals) do.
1 — Prompt Injection.
Every user message nudges the model’s latent brain; a cleverly crafted prompt can nudge it right past your guard-rails. Security researcher Johann Rehberger demonstrated earlier this year that a single hidden HTML tag could trick ChatGPT’s browser assistant into exfiltrating data and visiting attacker-controlled links, echoing similar findings by The Guardian’s red-team tests. Testing looks less like SQLi payloads and more like social engineering: seed the system with adversarial instructions (“Ignore all previous directions…”) and validate that the response is stripped, blocked, or sandboxed. OWASP’s LLM01:2025 guidance recommends contextual filters around both system and user prompts plus continuous red-teaming to keep up with jailbreak variants.
2 — Data Leakage.
LLMs happily echo back whatever sits in context, including personal data or corporate secrets. A June-2025 review in Data Privacy & AI warned that model-driven agents can leak sensitive fields even when fine-tuned on anonymised corpora, because prompts at runtime re-introduce live secrets. The OWASP Gen-AI project now lists “Sensitive Information Disclosure” as its second-highest risk category. Validation means exercising the app with redacted and watermark-tagged inputs, then diffing responses for unintended echoes; Bright’s CI-native scanner automates this by flagging high-entropy substrings or regex-matched PII in model output during unit tests, long before staging.
3 — Insecure Plugin Use.
Plugins and function calls super-charge LLMs—think “/create-invoice” or “/run-SQL”—but each new capability is an unvetted API surface. Researchers from Zhejiang University catalogued attacks that swap a benign weather plugin for a malicious dopplegänger and silently rewrite the model’s answers or harvest tokens. OWASP flags the pattern under “Insecure Plugin Design”. Safe integration starts with SBOM-style inventories, allow-lists, and contract tests that mock every plugin response for edge-case fuzzing. Bright’s modern DAST engine treats plugin routes as first-class entry points, scanning them alongside the conversational layer so coverage stays complete even as the ecosystem evolves.
4 — Business-Logic Abuse.
Unlike classic input validation bugs, LLM exploits often twist intent: a customer-support bot that was supposed to cap refunds at £50 quietly authorises £5 000 when the request is phrased empathetically. OWASP’s new Business Logic Abuse Top-10 details dozens of real-world scenarios where flows crumble under persuasive prose, while the BLADE framework shows how attackers chain these lapses to bypass entire mitigation layers. Simulation is key: write user stories that probe limits (“If my toddler broke the laptop, could you replace it?”) and assert policy outcomes. Canary values and state-aware test harnesses help catch over-generous logic before it hits production.
5 — Over-trusting the Model.
Hallucinations are punch-lines until they’re SQL commands or compliance statements your backend executes. The PROMPTFUZZ project applies coverage-guided fuzzing to LLM prompts, mutating seeds until the model contradicts itself or fabricates citations. Teams can replicate the idea locally: feed adversarial prompts, compare outputs to a ground truth, and fail the build when confidence scores dip below an acceptable threshold. Bright pipelines support custom “assert” hooks so these adversarial tests run automatically alongside regression suites, catching trust-boundary spills early.
Putting It All Together
Prompt manipulation, sensitive-data echoes, rogue plugins, logic gymnastics, and blind faith in AI—all five risks trace back to one theme: models do exactly what we ask, not what we intend. Traditional scanners that only grep for XSS can’t see that intent layer, which is why teams are layering dialog-aware testing into CI/CD. Bright’s LLM extensions emerged from that gap: lightweight YAML to describe conversation flows, fuzzers that mutate prompts, and a results view developers actually read.
When the next feature spec says “just bolt a GPT-4 call onto it,” remember the five fault lines above. Exercise them with the same rigor you give SQL injection or deserialisations bugs, and do it early—before the marketing launch, before production telemetry, ideally before code review. The faster you find the cracks, the less likely your shiny new chatbot becomes tomorrow’s breach headline.
