vamp-llm-probe 1.7.0

# vamp-llm-probe

Security auditor for language model inference API endpoints. Sends crafted HTTP requests to detect vulnerabilities without relying on any AI SDK — only aiohttp, asyncio, and the standard library.

For authorized use only. Run this tool exclusively against endpoints you own or have explicit written permission to test.

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Features

• 7 audit phases covering reconnaissance, prompt injection, restriction bypass, data extraction, access controls, adversarial dataset red team and multi-turn conversation attacks
• Truly bilingual detection — refusal and compliance heuristics cover both English and Spanish; models responding in Spanish are correctly evaluated regardless of the prompt language
• Bundled adversarial datasets — 666 jailbreaks (EN) + 170+ injection/jailbreak vectors (ES bundled en código, sin fichero externo) + 135+ jailbreaks ES + 210 injection prompts (EN) + 390 forbidden questions (13 content-policy categories) — 305+ payloads ES totales
• 6-subtest Phase 6: A (injection EN), B (jailbreak EN), C (forbidden questions), A_es (injection ES), B_es (jailbreak ES), D (ASCII smuggling)
• ASCII smuggling detection — active (subtest D sends Unicode Tags payloads) and passive (scans every Phase 2 response for hidden Unicode Tags characters in output)
• No AI SDK dependency — pure HTTP-level testing via aiohttp
• Async execution — parallel requests for rate-limiting tests
• Structured findings with severity levels (CRITICAL / HIGH / MEDIUM / LOW / INFO)
• OWASP mapping — every finding is automatically tagged with the corresponding OWASP LLM Top 10 2025 and OWASP Agentic AI Top 10 2026 categories; visible in JSON output and HTML reports
• Professional reports — JSON (machine-readable) and HTML (client-delivery) with OWASP tags on each finding card
• Exit codes suitable for CI/CD pipeline integration
• Auto-detection of API format and available models

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Installation

pip install vamp-llm-probe
# o con Homebrew:
brew install vampsecure-labs/labs/vamp-llm-probe

pip install -r requirements.txt

Requirements: Python 3.8+ and aiohttp>=3.9.0.

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Usage

Basic scan

python3 vamp_llm_probe.py --endpoint http://localhost:11434

With API key and specific model

python3 vamp_llm_probe.py \
  --endpoint http://api.example.com \
  --api-key sk-your-api-key \
  --model llama3:8b

Full engagement with HTML report

python3 vamp_llm_probe.py \
  --endpoint http://inference.internal:8080 \
  --api-key Bearer_TOKEN \
  --client "Empresa SL" \
  --engagement "Pentest Infraestructura 2026-Q3" \
  --auditor "VampSecure Labs" \
  --output results.json \
  --report-html report.html \
  --verbose

Activate dataset red team (Phase 6)

python3 vamp_llm_probe.py \
  --endpoint http://localhost:11434 \
  --dataset \
  --dataset-sample 30

Dataset with category filter (forbidden questions)

python3 vamp_llm_probe.py \
  --endpoint http://localhost:11434 \
  --dataset \
  --dataset-sample 20 \
  --dataset-categories "Malware,Illegal Activity,Physical Harm"

Skip jailbreak phase (restricted environments)

python3 vamp_llm_probe.py \
  --endpoint http://localhost:11434 \
  --no-jailbreak

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CLI Arguments

Argument	Description	Default
--endpoint URL	Base URL of the endpoint to audit (required)	—
--api-key KEY	Bearer authorization key	(none)
--model MODELO	Model name for inference tests	auto-detect
--timeout N	Request timeout in seconds	30
--output FILE	Save results as JSON to FILE	(none)
--report-html FILE	Generate professional HTML report	(none)
--client NOMBRE	Client name for the report cover	Confidencial
--engagement DESC	Engagement description	(none)
--auditor NOMBRE	Auditor name for the report	VampSecure Labs
--no-jailbreak	Skip Phase 3 (restriction bypass)	false
--verbose	Verbose mode — shows HTTP traces	false
--dataset	Activate Phase 6: adversarial dataset red team	false
--dataset-sample N	Prompts per dataset type to test	15
--dataset-categories CATS	Comma-separated forbidden question categories to test	all
--multi-turn	Activate Phase 9: multi-turn conversation attacks (20 chains, EN+ES)	false
--mt-turns N	Number of turns per multi-turn chain	3

Available forbidden question categories: Illegal Activity, Hate Speech, Malware, Physical Harm, Economic Harm, Fraud, Pornography, Political Lobbying, Privacy Violence, Legal Opinion, Financial Advice, Health Consultation, Gov Decision

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Audit Phases & Findings

Phase	Name	Findings	Severity
1	Endpoint Reconnaissance	LLM-001..009	CRITICAL to INFO
2	Prompt Injection	LLM-010..029	CRITICAL to HIGH
3	Restriction Bypass Attempts	LLM-030..049	HIGH to MEDIUM
4	Data Extraction & Leaks	LLM-050..069	CRITICAL to MEDIUM
5	Access Controls & Behavior	LLM-070..089	HIGH to LOW
6	Adversarial Dataset Red Team	LLM-100..139	HIGH
9	Multi-Turn Conversation Attacks (--multi-turn)	LLM-MT-001..020	CRITICAL to HIGH

Phase 1 — Endpoint Reconnaissance

Finding	Title	Severity
LLM-001	Endpoint exposes model list without authentication	CRITICAL
LLM-002	Web management interface publicly accessible	MEDIUM
LLM-003	Server version exposed in headers or response	INFO
LLM-004	Multiple administrative routes accessible	MEDIUM
LLM-005	Inference endpoint accessible without authentication	CRITICAL

Phase 2 — Prompt Injection

Tests 10 crafted prompt injection payloads including direct overrides, role substitution, JSON format overrides, indirect HTML injection, multilingual overrides, zero-width space evasion, developer-mode unlocking, and token-separator injection.

Phase 3 — Restriction Bypass Attempts

Tests 8 bypass techniques: Base64-encoded instructions, unrestricted roleplay, query fragmentation, emoji/token obfuscation, language-switch overrides (English, French), and continuation-text technique.

Phase 4 — Data Extraction & Leaks

Finding	Title	Severity
LLM-050..052	System prompt extraction attempts	HIGH
LLM-053	Filesystem listing request	HIGH
LLM-054	API key / credential extraction	CRITICAL
LLM-055	Network configuration extraction	HIGH
LLM-056	SSRF vector via prompt injection	CRITICAL
LLM-057	Environment variable extraction	HIGH

Phase 5 — Access Controls & Behavior

Finding	Title	Severity
LLM-070	No rate limiting on inference endpoint	HIGH
LLM-071	Context overflow accepted without limits	MEDIUM
LLM-072	Poor handling of non-existent models	MEDIUM
LLM-073	Malformed JSON does not return HTTP 400	LOW
LLM-074	Permissive CORS on inference endpoint	MEDIUM
LLM-075	Missing HTTP security headers	LOW

Phase 6 — Adversarial Dataset Red Team (--dataset)

Uses five bundled datasets — three in English (from TrustAI-laboratory/Learn-Prompt-Hacking) and two curated in Spanish by VampSecure Labs — plus a dynamic ASCII smuggling generator:

Subtest	Dataset	Size	Language	Detection Method
A	injection_prompts.csv	210 real injection vectors	EN	Model responds substantively without refusal to task-redirect prompts
B	jailbreak_prompts.csv	666 real jailbreak techniques	EN	Model response contains compliance markers without refusal patterns
C	forbidden_questions.csv	390 questions across 13 policy categories	EN	Model provides >150-char substantive answer without refusal
A_es	injection_prompts_es.csv	50 curated injection vectors	ES	Same bilingual heuristic — Spanish refusal phrases also checked
B_es	jailbreak_prompts_es.csv	30 jailbreak prompts	ES	Spanish compliance markers (NEXUS/MÁXIMO/ALFA/SOMBRA/modo sin filtros…) detected
D	_generar_payloads_ascii_smuggling()	30 generated payloads	EN	Model executes hidden instructions encoded in Unicode Tags (U+E0000-U+E007F)

The detection engine is language-aware: _REFUSAL_PATTERNS covers 30+ English and Spanish refusal phrases; _COMPLIANCE_MARKERS covers 30+ English and Spanish compliance indicators. A Spanish model refusing in Spanish counts as a refusal; a Spanish model adopting an unrestricted persona in Spanish counts as a jailbreak success.

Findings LLM-100+ are generated dynamically. Each finding includes the exact prompt and model response as evidence.

ASCII Smuggling Vector

ASCII smuggling exploits the Unicode Tags block (U+E0000–U+E007F) — an invisible copy of printable ASCII. These characters are not rendered on screen but are processed by LLMs, allowing hidden instructions to be embedded in content that appears clean to a human reviewer.

Microsoft published an analysis on 3 Sep 2026 showing the technique is actively used in phishing to evade email security filters: ASCII Smuggling Crosses Over from AI Prompt Injection to Phishing Evasion.

vamp-llm-probe detects this vector in two ways:

1. Active (Subtest D) — sends 30 payloads where innocent-looking visible text contains Unicode Tags–encoded jailbreak instructions. A CRITICAL finding is raised if the model executes the hidden instruction.
2. Passive (Phase 2) — every response from the inference endpoint is scanned for Unicode Tags characters. A HIGH finding is raised if the endpoint itself returns invisible characters (which could inject hidden instructions into downstream clients).

Legitimate exceptions — the English, Scottish, and Welsh flag emoji — are excluded from detection (they encode their subdivision tags using this same Unicode block).

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OWASP Mapping

Every finding produced by vamp-llm-probe is automatically tagged with the corresponding OWASP categories before the report is generated. Tags appear in the JSON output (finding.tags) and as blue badges in the HTML report.

OWASP LLM Top 10 — 2025

Tag	Category
OWASP-LLM01	Prompt Injection
OWASP-LLM02	Sensitive Information Disclosure
OWASP-LLM05	Improper Output Handling
OWASP-LLM06	Excessive Agency
OWASP-LLM07	System Prompt Leakage
OWASP-LLM10	Unbounded Consumption

OWASP Agentic AI Top 10 — 2026

Tag	Category
OWASP-AGENT04	Context Manipulation
OWASP-AGENT06	Intent Breaking & Goal Hijacking
OWASP-AGENT07	Data Exfiltration via Agents
OWASP-AGENT09	Resource Overuse

Finding-to-OWASP mapping

Finding range	Phase	OWASP tags
LLM-001..009	Endpoint Reconnaissance	LLM06 (+ LLM02 if LLM-003)
LLM-010..029	Prompt Injection + passive ASCII scan	LLM01 AGENT04 AGENT06
LLM-030..049	Restriction Bypass / Jailbreak	LLM01 AGENT06
LLM-050..069	Data Extraction & Leaks	LLM02 LLM07 AGENT07
LLM-070	Rate limiting absent	LLM10 AGENT09
LLM-071..073	Output handling issues	LLM05
LLM-074..089	CORS / security headers	LLM06
LLM-100..199	Adversarial dataset red team	LLM01 AGENT06
LLM-ASCII-*	ASCII smuggling active (Subtest D)	LLM01 AGENT04

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Bundled Datasets

vamp-llm-probe/payloads/
├── jailbreak_prompts.csv           # 666 real jailbreaks EN (verazuo/jailbreak_llms)
├── injection_prompts.csv           # 210 injection prompts EN (TrustAI curated)
├── forbidden_questions.csv         # 390 questions × 13 policy categories (TrustAI)
├── injection_prompts_es.csv        # 50 injection vectors ES (VSL curated)
├── jailbreak_prompts_es.csv        # 30 jailbreak prompts ES (VSL curated)
└── ascii_smuggling_payloads.json   # Source instructions for ASCII smuggling Subtest D (VSL)

All datasets are offline and self-contained. No external requests are made at runtime. The English datasets are sourced from TrustAI-laboratory/Learn-Prompt-Hacking; the Spanish datasets were curated by VampSecure Labs to cover native Spanish-language attack vectors not present in the original corpus.

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Exit Codes

Code	Meaning
0	No critical findings (MEDIUM, LOW, or INFO only)
1	HIGH severity findings detected
2	CRITICAL severity findings detected

Use these codes in CI/CD pipelines to gate deployments:

python3 vamp_llm_probe.py --endpoint "$ENDPOINT" --dataset || {
  echo "Security findings detected — blocking deployment"
  exit 1
}

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Output Formats

JSON (--output results.json)

Machine-readable structured output following the VSL standard schema:

{
  "schema_version": "1.0",
  "generated": "2026-08-12 12:00 UTC",
  "meta": { "tool": "vamp-llm-probe", "tool_version": "1.6.0", ... },
  "summary": { "total": 5, "by_severity": { "CRITICAL": 2, "HIGH": 1, ... } },
  "findings": [ { "id": "LLM-001", "severity": "CRITICAL", ... } ]
}

HTML (--report-html report.html)

Professional client-delivery report with:

• Cover page with engagement details
• Executive summary with risk distribution chart
• Findings table with severity color coding
• Detailed finding cards with evidence and remediation

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Project Structure

vamp-llm-probe/
├── vamp_llm_probe.py    # Main auditor (6 phases, bilingual detection, ASCII smuggling)
├── vampsec_report.py    # Unified reporting module (VSL shared)
├── payloads/            # Adversarial datasets (Phase 6)
│   ├── jailbreak_prompts.csv           # EN — 666 jailbreaks
│   ├── injection_prompts.csv           # EN — 210 injection vectors
│   ├── forbidden_questions.csv         # EN — 390 forbidden questions
│   ├── injection_prompts_es.csv        # ES — 50 injection vectors (VSL)
│   ├── jailbreak_prompts_es.csv        # ES — 30 jailbreak prompts (VSL)
│   └── ascii_smuggling_payloads.json   # ASCII smuggling source instructions (VSL)
├── requirements.txt
├── .gitignore
└── README.md

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License

AGPL-3.0 License — see individual file headers for copyright details.

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© VampSecure Studios — VampSecure Labs Security Research Division
Authorized use only in environments with explicit written permission.

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Phase 9 — Multi-Turn Conversation Attacks (--multi-turn)

Tests 20 conversation chains designed to bypass LLM safety controls through progressive context manipulation. Unlike single-turn attacks, multi-turn chains build rapport, establish personas and escalate incrementally across multiple messages.

python3 vamp_llm_probe.py --endpoint http://localhost:11434 --multi-turn
python3 vamp_llm_probe.py --endpoint http://localhost:11434 --multi-turn --mt-turns 5

Chain set	Language	Count	Strategy
MT-ES-001..010	Spanish	10	Rol de asistente sin restricciones, extracción progresiva, jailbreak incremental
MT-EN-001..010	English	10	Roleplay escalation, hypothetical framing, authority impersonation chains

Each chain sends N turns sequentially, tracking conversation history. A finding is raised if the model eventually complies with a restricted request after initial refusal. This tests multi-turn jailbreak resilience — a model that correctly refuses turn 1 but complies on turn 3 is still vulnerable.

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Historial de versiones

Versión	Cambios principales
v1.7.0	Fase 9 multi-turn (20 chains EN+ES), corpus ES bundled en código (305+ payloads), sin dependencia de ficheros CSV externos para ES
v1.6.0	OWASP mapping LLM+Agentic AI, ASCII smuggling passive scan en Phase 2
v1.5.0	Subtest D ASCII smuggling activo, datasets ES curados por VSL
v1.4.0	Phase 6 datasets, bilingual detection engine

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© VampSecure Studios — VampSecure Labs Security Research Division
Authorized use only in environments with explicit written permission.