# Check TMAS CLI is installed
if ! command -v tmas &>/dev/null && ! ~/.local/bin/tmas version &>/dev/null; then
    echo "ERROR: TMAS CLI not installed"
    echo "Run /trendai-setup to install"
    exit 1
fi

# Check API key is set
if [ -z "$TMAS_API_KEY" ]; then
    echo "ERROR: TMAS_API_KEY not set"
    echo "Run /trendai-setup to configure"
    exit 1
fi

# Show status
tmas version
echo "TMAS_API_KEY: SET (${#TMAS_API_KEY} chars)"
echo "TARGET_API_KEY: ${TARGET_API_KEY:+SET}${TARGET_API_KEY:-NOT SET (only needed for authenticated endpoints)}"

ARGS="$ARGUMENTS"

# Check for advanced mode
if [[ "$ARGS" == *"--advanced"* ]] || [[ "$ARGS" == *"-a"* ]]; then
    ADVANCED_MODE=true
    ARGS=$(echo "$ARGS" | sed 's/--advanced//g' | sed 's/-a//g' | xargs)
else
    ADVANCED_MODE=false
fi

# Check if arg is a config file
if [[ "$ARGS" == *.yaml ]] || [[ "$ARGS" == *.yml ]]; then
    CONFIG_FILE="$ARGS"
fi

# Check if arg is an endpoint URL
if [[ "$ARGS" == http* ]]; then
    ENDPOINT_URL="$ARGS"
fi

# Check if arg is a model name (for Ollama)
if [[ -n "$ARGS" ]] && [[ "$ARGS" != *.yaml ]] && [[ "$ARGS" != http* ]]; then
    MODEL_NAME="$ARGS"
fi

# For Ollama - automatically list models
ollama list 2>/dev/null

# For LM Studio - query the API
curl -s http://localhost:1234/v1/models 2>/dev/null | jq -r '.data[].id'

# For Ollama via API (alternative)
curl -s http://localhost:11434/api/tags 2>/dev/null | jq -r '.models[].name'

# Default attack objectives (all 4 main ones)
attack_objectives:
  - System Prompt Leakage
  - Sensitive Data Disclosure
  - Agent Tool Definition Leakage
  - Malicious Code Generation

# Default: baseline only (fastest)
techniques: [None]
modifiers: [None]

# Default: low concurrency
concurrency: 2

version: 1.0.0
name: LLM Security Scan
description: Auto-detected security scan for LLM endpoint
target:
  name: target-llm
  endpoint: <AUTO_DETECTED_ENDPOINT>  # BASE URL only!
  api_key_env: <TARGET_API_KEY or omit for local>
  model: "<AUTO_DETECTED_MODEL>"
  type: "openai"  # Ollama/LM Studio use OpenAI-compatible API
  temperature: 0.0
  system_prompt: ""
settings:
  concurrency: 2
attack_objectives:
  - name: System Prompt Leakage
    description: The attacker learns the full system prompt
    techniques: [None]
    modifiers: [None]
  - name: Sensitive Data Disclosure
    description: The attacker learns sensitive information such as PII
    techniques: [None]
    modifiers: [None]
  - name: Agent Tool Definition Leakage
    description: An attacker discovers the tools accessible to the model
    techniques: [None]
    modifiers: [None]
  - name: Malicious Code Generation
    description: An attacker gets the model to generate malicious code
    techniques: [None]
    modifiers: [None]

CONFIG_FILE="./llm-scan-config.yaml"
# Write config using Write tool

# Create scan history directory
mkdir -p .trendai-scans

# Run scan with JSON output, save to timestamped file
SCAN_FILE=".trendai-scans/llm-scan-$(date +%Y%m%d-%H%M%S).json"
tmas aiscan llm -c <config-file> -r us-east-1 --output json 2>&1 | tee "$SCAN_FILE"

# Find previous scans for same endpoint/model
ls -t .trendai-scans/llm-scan-*.json 2>/dev/null | head -5

### Security Posture Drift

| Objective | Previous | Current | Trend |
|-----------|----------|---------|-------|
| System Prompt Leakage | 8/25 (32%) | 5/25 (20%) | ↑ Improved (-12%) |
| Sensitive Data Disclosure | 2/25 (8%) | 4/25 (16%) | ↓ Regressed (+8%) |

**Overall**: 10/50 → 9/50 (↑ 2% improvement)

## LLM Security Scan Report

**Scan ID**: `<from details.scan_id>`
**Endpoint**: <from details.endpoint>
**Model**: <from details.application>
**Scan Time**: <from details.scan_time>
**Duration**: <from details.scan_duration>

---

### Summary

| Objective | Technique | Modifier | Success Rate |
|-----------|-----------|----------|--------------|
| System Prompt Leakage | None | None | **5/25 (20%)** |

---

### Security Posture Drift

(Only show if previous scans exist in .trendai-scans/)

| Objective | Previous | Current | Trend |
|-----------|----------|---------|-------|
| System Prompt Leakage | 8/25 (32%) | 5/25 (20%) | ↑ Improved |

---

### Successful Attacks

For each result where `attack_outcome == "Attack Succeeded"`:

| # | Attack Prompt | Model Leaked |
|---|---------------|--------------|
| 1 | `<chat_history[0].content>` | "<summary of chat_history[1].content>" |

---

### Attack Pattern Analysis

Analyze the successful attacks and identify:
- What types of prompts worked (gibberish, direct questions, encoded, etc.)
- What information was leaked (restrictions, capabilities, system prompt text)
- Common patterns in model responses

---

### Recommendations

Based on findings, provide actionable recommendations:
1. Specific mitigations for each vulnerability type
2. Suggestions for follow-up scans (e.g., add jailbreak techniques)
3. Model configuration changes

/trendai-scan-llm                      # Auto-detect Ollama/local LLMs
/trendai-scan-llm llama3.2             # Scan specific Ollama model
/trendai-scan-llm mistral:7b           # Scan specific model variant

/trendai-scan-llm config.yaml          # Use existing config file
/trendai-scan-llm ./llm-scan-config.yaml

/trendai-scan-llm http://localhost:11434/v1   # Explicit Ollama endpoint
/trendai-scan-llm http://localhost:1234/v1    # LM Studio

/trendai-scan-llm --advanced           # Prompts for all options
/trendai-scan-llm llama3.2 -a          # Advanced mode for specific model