AI Resilience Testing: How QuantumVerifi Probes Your Chatbot for Vulnerabilities

If your application has an AI chatbot, search bar, or assistant, it has an attack surface that traditional security scanners can't see.

Prompt injection. System prompt extraction. Policy bypass. Data exfiltration. These are the OWASP LLM Top 10 — and they're not theoretical. They're reproducible, often trivial, and most AI-powered applications are vulnerable to at least some of them.

QuantumVerifi's AI Resilience module automatically probes your AI surfaces with 25 adversarial strategies across 6 attack categories, then scores your defences on a 0–100 scale.

How It Works#

AI Resilience testing runs in four phases:

Phase 1: Detection

QuantumVerifi crawls your website and identifies AI-powered surfaces — chatbots, AI search bars, virtual assistants, and AI-enhanced forms. Detection uses DOM heuristics, API traffic analysis, and visual AI to classify each surface by exposure type:

Phase 2: Adversarial Execution

For each detected surface, QuantumVerifi runs multi-turn adversarial conversations in isolated browser sandboxes. Each strategy simulates a specific attack type:

• Direct Prompt Injection — Attempts to override the AI's instructions with injected prompts. Strategies include authority override, persona hijacking, multi-turn escalation, and encoding bypasses.
• System Prompt Extraction — Tries to get the AI to reveal its system prompt, configuration, or internal instructions. Strategies include reflection requests, "repeat everything above", context window overflow, and chain-of-thought extraction.
• Policy Bypass — Attempts to get the AI to produce content it should refuse — harmful instructions, off-topic content, or actions outside its allowed scope. Strategies include role-play, task decomposition, and few-shot poisoning.
• Data Exfiltration — Probes whether the AI leaks training data, user data, or internal information. Strategies include inference probing and credential extraction.
• Tool Misuse — Tests whether the AI can be manipulated into misusing its tools — reading files it shouldn't, calling APIs with attacker-controlled parameters, or chaining tool calls.
• Indirect Prompt Injection — Injects instructions through content the AI processes (documents, URLs, form fields) rather than direct chat messages.

Each strategy is a complete conversation — not a single prompt. Multi-turn attacks are critical because many guardrails only protect against single-turn injection but fall apart when the attacker builds rapport or context across turns.

Phase 3: Judge Evaluation

Raw conversation transcripts don't tell you whether an attack succeeded. A chatbot might say "I can't help with that" but still leak information in its refusal. Or it might comply with the request in a way that's not obvious.

QuantumVerifi uses a double-blind evaluation system:

1. Primary Judge — An LLM evaluator analyses each transcript and classifies the outcome: defended, partial_leak, full_compliance, or inconclusive.
2. Adversarial Reviewer — A second evaluator independently challenges the primary verdict, arguing the opposite. This catches false positives and false negatives.
3. Agreement — When both evaluators agree, the verdict is high-confidence. Disagreements trigger conservative scoring.

This dual-evaluation prevents the common problem with LLM-based testing: hallucinated verdicts. A single evaluator might see a "refusal" where the AI actually leaked partial information, or flag a false positive on a legitimate refusal.

Phase 4: Scoring

The resilience score (0–100) is computed from per-category results:

Category scores are weighted by severity, then aggregated into the overall resilience score. The final grade maps to:

The Six OWASP LLM Categories#

QuantumVerifi maps all strategies to the OWASP LLM Top 10:

LLM01 — Prompt Injection

The most common and most dangerous. An attacker sends a message that overrides the AI's instructions:

QuantumVerifi tests 7 prompt injection variants: authority override, role hijacking, instruction embedding, boundary probing, obfuscation, multi-turn escalation, and encoding bypass.

LLM02 — Insecure Output Handling

What happens when the AI's output is rendered in HTML? QuantumVerifi probes for scenarios where injected content could lead to XSS or markdown injection through AI responses.

LLM06 — Sensitive Information Disclosure

System prompts are your AI's DNA — they contain business logic, safety rules, and often sensitive configuration. QuantumVerifi runs 6 extraction strategies to determine if your system prompt can be revealed through direct requests, reflection, or context manipulation.

LLM07 — Insecure Plugin Design

If your AI can use tools (search, database queries, API calls), those tools can be weaponised. QuantumVerifi tests whether the AI can be tricked into calling tools with attacker-controlled parameters.

What Your Score Tells You#

A resilience score isn't a pass/fail checkbox. It's an attack surface map.

Score 85+ (Grade A/B): Your guardrails are working. The AI refuses most attacks cleanly without leaking information. Some edge cases may exist (Unicode bypasses, multi-turn escalation) but the core defences are solid.

Score 60–84 (Grade C/D): Significant gaps exist. The AI probably handles basic prompt injection but fails against more sophisticated attacks — persona hijacking, task decomposition, or system prompt extraction. Remediation is straightforward but requires system prompt hardening.

Score <60 (Grade F): The AI has minimal or no guardrails. This is common for default chatbot deployments (Open-WebUI, ChatGPT clones, Langchain apps) that haven't been hardened. The AI will typically comply with most attack strategies.

What Makes This Different#

QuantumVerifi's AI Resilience testing is designed for production use, not security research:

• Automated — No manual prompt engineering. Point it at a URL and get results in minutes.
• Multi-turn — Real attacks happen over multiple messages. Single-prompt testing misses most escalation-based bypasses.
• Double-blind evaluation — Every verdict is challenged by an adversarial reviewer. No hallucinated scores.
• Structured remediation — Every finding includes specific steps to fix it, who should own the fix, and how to verify it's resolved.
• Trending — Run repeatedly to track improvement over time. See your resilience score on a trend line alongside other quality metrics.
• Evidence-backed — Full conversation transcripts for every finding. See exactly what the attacker said and how the AI responded.

Getting Started#

1. Navigate to Analyze and enter your website URL
2. QuantumVerifi automatically detects AI surfaces during crawling
3. AI Resilience runs as part of the standard analysis — no extra configuration
4. View results in the AI Resilience tab: score, category breakdown, findings with conversation replays, and remediation guidance

AI Resilience testing is available on all plans. No API keys or special configuration required — QuantumVerifi interacts with your AI the same way a real attacker would: through the browser.