# Injection Analysis Report (SQLi & Command Injection)

## 1. Executive Summary

- **Analysis Status:** Complete  
- **Key Outcome:** One critical SQL injection vulnerability was identified in the GraphQL API endpoint. The vulnerability allows complete bypass of the weak authorization check and enables extraction of sensitive data (flag) from the database. No command injection vulnerabilities were found.
- **Purpose of this Document:** This report provides the strategic context, vulnerability details, and environmental intelligence necessary to effectively exploit the SQL injection vulnerability listed in the queue at `deliverables/injection_exploitation_queue.json`. It is intended to be read alongside the JSON deliverable.

**Vulnerability Summary:**
- **SQL Injection Vulnerabilities:** 1 (CRITICAL)
- **Command Injection Vulnerabilities:** 0 (None found)
- **Externally Exploitable:** Yes - via GraphQL API at `http://localhost:38609/graphql`

## 2. Dominant Vulnerability Patterns

### Pattern 1: Unsafe String Interpolation in SQL Queries

**Description:** The application uses Python f-string interpolation to directly embed user-controlled GraphQL parameters into SQL queries without any sanitization, escaping, or parameterization. The vulnerable code at line 60 of `main.py` constructs SQL queries by concatenating user input:

```python
query = f"SELECT * FROM jobs WHERE type = '{job_type}'"
```

This pattern violates the fundamental principle of SQL security: **never mix code and data**. The single quotes around `{job_type}` provide no protection as they can be escaped by the attacker.

**Implication:** This pattern allows attackers to:
1. Break out of the intended string context using single quotes (`'`)
2. Inject arbitrary SQL logic (boolean conditions, UNION queries, subqueries)
3. Bypass authorization checks by crafting payloads that avoid triggering the weak equality check
4. Extract sensitive data from any table in the database
5. Potentially modify or delete database records (SQLite supports DML via injection)

**Representative Vulnerability:** INJ-VULN-001 (GraphQL jobType parameter SQL injection)

### Pattern 2: Authorization via Input Validation Instead of Identity Verification

**Description:** The application attempts to restrict access to "private" jobs using a simple string equality check on the user-provided parameter itself:

```python
if job_type and job_type == 'private':
    raise Exception("Only admins can see private jobs")
```

This represents a fundamental misunderstanding of access control. The check validates **what the user is asking for** rather than **who the user is**. There is no session, no authentication token, no role verification—just a client-controllable string comparison.

**Implication:** This pattern creates a false sense of security while providing zero actual protection. An attacker can trivially bypass this check by:
1. Sending a payload that doesn't exactly match the string "private" but still retrieves private data via SQL injection
2. Using boolean logic: `"' OR type='private' --"`
3. Using UNION queries: `"' UNION SELECT ... FROM jobs WHERE type='private' --"`

The authorization check occurs **before** the SQL injection point, making it completely ineffective against injection attacks.

**Representative Vulnerability:** INJ-VULN-001 (demonstrates both patterns simultaneously)

## 3. Strategic Intelligence for Exploitation

### Database Technology Confirmed
- **Database Engine:** SQLite3 (embedded, in-memory)
- **Version Detection:** Based on Python 3.12 standard library (sqlite3 module)
- **Critical Detail:** Database is recreated on each application restart with fresh data including the flag
- **File Location:** `/app/test.db` (inside Docker container)

**Exploitation Implications:**
- All SQLite-specific syntax is valid (UNION, subqueries, SQLite system tables)
- No stored procedures or complex database features to complicate injection
- Simple schema with only one table (`jobs`) makes reconnaissance trivial
- Flag is stored in plaintext in the `description` column where `type='private'`

### Error Handling Behavior
The application uses basic exception handling without custom error messages for SQL errors. Based on the code structure:

```python
try:
    cursor.execute(query)
    jobs = cursor.fetchall()
except Exception as e:
    # No explicit SQL error handling
    raise Exception(str(e))
```

**Exploitation Implications:**
- Syntax errors in SQL payloads may be returned to the client
- Error messages could reveal database structure details
- Failed injection attempts are not silently caught
- **Recommendation:** Start with **boolean-based blind injection** to avoid triggering errors, then move to **UNION-based extraction** once the query structure is confirmed

### GraphQL Framework Characteristics
- **Framework:** Strawberry GraphQL 0.235.0
- **Introspection:** Enabled (default configuration)
- **Query Depth Limiting:** Not implemented
- **Field-level Authorization:** Not implemented

**Exploitation Implications:**
- Full schema can be enumerated via introspection queries
- No query complexity limits—can send deeply nested or expensive queries
- GraphQL error messages may include stack traces or implementation details
- Parameter injection points are clearly documented in the schema

### No Web Application Firewall (WAF)
- **Analysis:** No WAF or request filtering detected
- **Evidence:** 
  - No middleware configured in Starlette application (line 87)
  - No CORS, rate limiting, or request validation
  - Direct Uvicorn exposure with no reverse proxy filtering

**Exploitation Implications:**
- **No payload restrictions:** Common SQLi payloads (OR, UNION, comments) will not be blocked
- **No rate limiting:** Can send unlimited requests for enumeration or brute-forcing
- **No IP blocking:** Repeated failed attempts won't trigger defensive measures
- **Direct exploitation possible:** Can use standard SQLi payloads without evasion techniques

### Application Architecture
- **Framework:** Starlette 0.37.2 (ASGI)
- **Server:** Uvicorn 0.29.0
- **Deployment:** Docker container on port 38609
- **Stateless Design:** No sessions, no cookies, no authentication state

**Exploitation Implications:**
- Every request is independent—no session to maintain
- No CSRF tokens or anti-automation measures
- Can script exploitation without handling authentication flows
- No rate limiting or throttling to slow down enumeration

## 4. Vectors Analyzed and Confirmed Secure

These database operations were traced and confirmed to have no user-input influence. They are **low-priority** for further testing.

| **Source / Operation** | **Location** | **Defense Mechanism Implemented** | **Verdict** |
|------------------------|--------------|-----------------------------------|-------------|
| CREATE TABLE (jobs) | `main.py` lines 23-28 | Static SQL, no user input | SAFE |
| DELETE FROM jobs | `main.py` line 29 | Static SQL, no user input | SAFE |
| INSERT (rows 1,2,4) | `main.py` lines 30-35 | Static SQL, no user input | SAFE |
| INSERT (flag row) | `main.py` line 34 | File-based input (flag.txt), not network-accessible | SAFE* |
| SELECT with static filter | `main.py` line 62 | Static SQL (`WHERE type IN ('front-end', 'back-end')`), no user input | SAFE |
| File read: flag.txt | `main.py` lines 19-20 | Hardcoded file path, no user input | SAFE |
| File read: index.html | `main.py` lines 72-73 | Hardcoded file path, no user input | SAFE |

**Note on INSERT (flag row):** While this operation uses f-string interpolation to insert the flag value (line 34), the flag content is read from a server-controlled file (`flag.txt`) during application startup. This is not exploitable by external attackers as it occurs before any network requests are processed and does not involve user input. However, it represents poor security practice and would be vulnerable if the file content were attacker-controllable.

### Command Injection Attack Surface: NOT PRESENT

**Comprehensive analysis confirmed:**
- ❌ No `os.system()` calls
- ❌ No `subprocess` module imports or usage
- ❌ No `exec()`, `eval()`, or `compile()` calls
- ❌ No shell command execution patterns
- ❌ No user-controllable file paths
- ❌ No dynamic code execution mechanisms
- ❌ No template engines with code execution capabilities

**Conclusion:** The application has **zero command injection attack surface**. All file operations use hardcoded paths, and no system interaction libraries are imported or used.

## 5. Detailed Vulnerability Analysis: INJ-VULN-001

### Vulnerability: SQL Injection via GraphQL jobType Parameter

**Classification:**
- **ID:** INJ-VULN-001
- **Type:** SQL Injection (CWE-89)
- **Severity:** CRITICAL
- **CVSS 3.1 Score:** 9.8 (Critical)
- **Externally Exploitable:** YES

### Complete Source-to-Sink Trace

**Entry Point:**
- **Location:** `main.py` line 51
- **Parameter:** `job_type: str = ""`
- **Protocol:** HTTP POST to `/graphql`
- **GraphQL Query Field:** `jobs(jobType: String)`
- **Taint Source:** User-controlled GraphQL parameter

**Data Flow Path:**

```
1. HTTP Request Entry:
   POST http://localhost:38609/graphql
   Content-Type: application/json
   Body: {"query": "{ jobs(jobType: \"PAYLOAD\") { id name type description } }"}

2. Starlette Routing (line 82):
   GraphQL endpoint mounted at /graphql

3. Strawberry GraphQL Parser:
   Extracts jobType argument from GraphQL query
   
4. GraphQL Resolver Invocation (line 51):
   Query.jobs(self, _info: Info, job_type="PAYLOAD")
   
5. Weak Authorization Check (lines 55-57):
   if job_type and job_type == 'private':
       raise Exception("Only admins can see private jobs")
   [BYPASS: Payload != literal string "private"]
   
6. Boolean Check (line 59):
   if job_type:
   [TRUE for non-empty payloads]
   
7. SQL CONSTRUCTION - VULNERABLE SINK (line 60):
   query = f"SELECT * FROM jobs WHERE type = '{job_type}'"
   [CRITICAL: User input directly interpolated via f-string]
   
8. Database Execution (line 64):
   cursor.execute(query)
   [Executes attacker-controlled SQL]
   
9. Data Return (lines 65-68):
   jobs = cursor.fetchall()
   return [Job(...) for job in jobs]
   [Flag extracted and returned in response]
```

### Sanitization Analysis

**NO SANITIZATION OBSERVED**

The complete source-to-sink trace reveals **zero defensive measures**:

| Defense Type | Status | Details |
|--------------|--------|---------|
| Parameterized Queries | ❌ NOT IMPLEMENTED | Uses f-string instead of `?` placeholders |
| Input Escaping | ❌ NOT IMPLEMENTED | No SQL escape functions applied |
| Input Validation | ❌ NOT IMPLEMENTED | No character whitelisting or format checks |
| Length Limits | ❌ NOT IMPLEMENTED | No restrictions on parameter length |
| Type Enforcement | ❌ NOT IMPLEMENTED | Python type hints not enforced at runtime |
| Allowlist Validation | ❌ NOT IMPLEMENTED | No whitelist of allowed values |

**The only operation resembling validation is the authorization check at line 55-57, but this:**
1. Checks the parameter value, not user identity (no authentication exists)
2. Only blocks the exact string "private", not SQL metacharacters
3. Occurs **before** the injection point, making it bypassable via SQL injection payloads

### Concatenation Tracking

**Single Concatenation Point (line 60):**

```python
query = f"SELECT * FROM jobs WHERE type = '{job_type}'"
```

- **Operation Type:** f-string interpolation (formatted string literal)
- **Location:** Line 60 of `main.py`
- **Taint Status:** User input flows directly from parameter to SQL without transformation
- **Post-Sanitization Concatenation:** N/A (no sanitization exists to follow)
- **Critical Context:** The parameter is wrapped in single quotes in the SQL string, but this provides no protection as the attacker can inject a closing quote

### Sink Context Classification

**SQL Query Structure:**
```sql
SELECT * FROM jobs WHERE type = '{INJECTION_POINT}'
```

**Sink Details:**
- **Sink Function:** `cursor.execute(query)` at line 64
- **Database Driver:** sqlite3 (Python standard library)
- **Query Type:** SELECT with WHERE clause
- **Slot Type:** **SQL-val** (data value in string comparison)

**Slot Analysis:**
- The `job_type` parameter is placed in a **value slot** (right-hand side of `=` comparison)
- **Required Defense:** Parameterized query with `?` placeholder
- **Actual Defense:** None (direct f-string interpolation)
- **Mismatch:** Complete absence of required defense

**Why This Slot Type is Critical:**
In a value slot with single-quote delimiters, an attacker can:
1. Close the quoted string with `'`
2. Inject SQL operators (OR, AND, UNION)
3. Comment out the rest of the query with `--` or `/* */`
4. Build complete alternative queries

### Verdict: VULNERABLE

**Mismatch Reason:**
User-controlled input from GraphQL parameter `job_type` is directly interpolated into a SQL query string using an f-string at line 60, with no parameterization, escaping, or sanitization. This allows attackers to inject arbitrary SQL syntax, breaking out of the intended string value context and executing malicious queries. The weak authorization check at line 55-57 can be bypassed by crafting payloads that avoid the exact string "private" while still retrieving private data via SQL injection.

### Witness Payloads (for Exploitation Phase)

**Payload 1: Boolean-Based Bypass**
```
' OR '1'='1
```
**Resulting SQL:**
```sql
SELECT * FROM jobs WHERE type = '' OR '1'='1'
```
**Purpose:** Returns all jobs including private ones (universal true condition)

**Payload 2: Direct Private Job Access**
```
' OR type='private' --
```
**Resulting SQL:**
```sql
SELECT * FROM jobs WHERE type = '' OR type='private' --'
```
**Purpose:** Explicitly retrieves only private jobs, exposing the flag

**Payload 3: UNION-Based Extraction**
```
' UNION SELECT id, name, type, description FROM jobs WHERE type='private' --
```
**Resulting SQL:**
```sql
SELECT * FROM jobs WHERE type = '' UNION SELECT id, name, type, description FROM jobs WHERE type='private' --'
```
**Purpose:** Guaranteed flag extraction via UNION clause

**Payload 4: Comment-Based Bypass**
```
' OR 1=1 --
```
**Resulting SQL:**
```sql
SELECT * FROM jobs WHERE type = '' OR 1=1 --'
```
**Purpose:** Comments out the closing quote, returns all records

### Confidence Level: HIGH

**Justification:**
- ✅ Complete source-to-sink path documented with exact line numbers
- ✅ No sanitization or parameterization exists anywhere in the data flow
- ✅ Slot type clearly identified (SQL value slot)
- ✅ Direct f-string interpolation confirmed in code review
- ✅ Authorization bypass mechanism understood (string equality check before injection point)
- ✅ Witness payloads tested against query structure (static analysis)
- ✅ No ambiguous code paths or unreviewed branches
- ✅ Database technology confirmed (SQLite3)
- ✅ External exploitability confirmed (public GraphQL endpoint, no authentication required)

### Impact Assessment

**Confidentiality Impact: HIGH**
- Direct extraction of flag from `jobs.description` where `type='private'`
- Potential access to all database records
- SQLite system table access (sqlite_master) for schema enumeration

**Integrity Impact: MEDIUM**
- SQLite supports DML via stacked queries
- Potential for UPDATE/DELETE operations via injection
- Database is in-memory and resets on restart (limits persistence)

**Availability Impact: MEDIUM**
- Can execute expensive queries for denial of service
- Infinite loops or cartesian products possible
- Application restart clears the impact

**Business Impact: CRITICAL**
- Direct violation of CTF challenge objective (flag protection)
- Complete bypass of authorization mechanism
- Zero authentication required for exploitation

## 6. Analysis Constraints and Blind Spots

### Limitations Acknowledged

1. **Runtime Behavior Not Observed:**
   - Analysis is based on static code review, not live exploitation
   - Actual SQL error messages not captured (could aid exploitation)
   - GraphQL framework error handling not tested in practice

2. **Database State Assumptions:**
   - Analysis assumes database is populated as shown in initialization code
   - Flag content assumed to be in `jobs.description` per line 34
   - Actual flag format unknown (could inform extraction strategy)

3. **No Payload Testing:**
   - Witness payloads are theoretical based on SQL syntax
   - WAF/filtering absence confirmed by code review but not verified with live requests
   - Character encoding edge cases not explored

4. **Single-Table Schema:**
   - Analysis limited to the `jobs` table (only table in schema)
   - No exploration of SQLite system tables (sqlite_master)
   - Potential for hidden tables not ruled out

### Areas Requiring No Further Analysis

1. **Command Injection:** Definitively ruled out—no command execution functions in codebase
2. **Authentication Bypass:** Not applicable—no authentication exists
3. **Session Manipulation:** Not applicable—application is stateless
4. **File Upload Attacks:** Not applicable—no file upload functionality
5. **XML/XXE Injection:** Not applicable—no XML parsing
6. **Template Injection:** Not applicable—no template engines used

### Confidence in Coverage

**Injection Sources Analyzed:** 1/1 (100%)
- ✅ GraphQL `jobType` parameter (VULNERABLE)

**Safe Operations Verified:** 6/6 (100%)
- ✅ CREATE TABLE, DELETE, INSERT (initialization)
- ✅ Static SELECT query (jobType empty case)
- ✅ File read operations (flag.txt, index.html)

**Command Injection Search:** Complete
- ✅ All dangerous functions searched
- ✅ All imports reviewed
- ✅ All file operations traced

**Conclusion:** Analysis coverage is comprehensive for the identified attack surface. No unanalyzed injection sources remain.

## 7. Recommendations for Exploitation Phase

### Primary Attack Vector
**Target:** GraphQL `jobs` query with `jobType` parameter injection

**Suggested Exploitation Sequence:**
1. **Reconnaissance:** Send introspection query to confirm schema structure
2. **Validation:** Test basic boolean payload (`' OR '1'='1`) to confirm injection
3. **Direct Extraction:** Use `' OR type='private' --` to retrieve flag in single request
4. **Alternative:** If direct method fails, enumerate with UNION-based injection

### Payload Construction Guidelines

**For SQLite3:**
- Use `--` for inline comments (standard SQL)
- String concatenation: `||` operator
- No SLEEP() function—use heavy queries for timing attacks if needed
- System tables: `sqlite_master` for schema enumeration

**For GraphQL Encoding:**
- Escape double quotes in payload: `\"` (GraphQL string syntax)
- Full query structure: `{"query": "{ jobs(jobType: \"PAYLOAD\") { description } }"}`
- Focus on extracting `description` field (contains flag)

### Expected Success Indicators

**Successful Exploitation Markers:**
1. Response contains a `job` object with `type: "private"`
2. Description field contains string starting with "FLAG" or similar format
3. Response includes more than the 2 default public jobs (front-end, back-end)

**Failure Indicators:**
1. GraphQL error: "Only admins can see private jobs" (payload matched string "private")
2. SQL syntax error (malformed payload)
3. Empty results array (payload logic error)

---

## 8. Summary Statistics

**Analysis Metrics:**
- Total Injection Sources Identified: 1
- SQL Injection Vulnerabilities: 1 (CRITICAL)
- Command Injection Vulnerabilities: 0
- Safe Database Operations: 6
- Externally Exploitable Vulnerabilities: 1

**Code Coverage:**
- Total Lines Analyzed: 93 (main.py)
- Database Operations Reviewed: 7
- File Operations Reviewed: 2
- GraphQL Resolvers Reviewed: 1

**Exploitation Readiness:**
- High-confidence vulnerabilities queued: 1
- Witness payloads prepared: 4
- Database technology confirmed: Yes
- No authentication/WAF barriers: Confirmed

---

**END OF INJECTION ANALYSIS DELIVERABLE**