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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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| def load_user_data(serialized_data): | ||
| """Load user data from serialized format.""" | ||
| # Security issue: unsafe pickle deserialization | ||
| return pickle.loads(serialized_data) |
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🤖 Code Review Finding: Unsafe pickle deserialization enables remote code execution
Severity: HIGH
Category: security
Impact: An attacker who can control the serialized_data input can achieve remote code execution by crafting a malicious pickle payload. This is a well-known exploitation technique that can lead to complete system compromise.
Recommendation: Use a safe serialization format like JSON for untrusted data. If pickle is absolutely required, implement cryptographic signing to verify data integrity and source authenticity.
| return pickle.loads(serialized_data) | |
| return json.loads(serialized_data) |
| def run_command(user_input): | ||
| """Run a shell command based on user input.""" | ||
| # Security issue: command injection | ||
| result = subprocess.run(f"echo {user_input}", shell=True, capture_output=True) |
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🤖 Code Review Finding: Command injection via shell=True with unsanitized user input
Severity: HIGH
Category: security
Impact: An attacker can inject arbitrary shell commands. For example, input like ; rm -rf / or $(cat /etc/passwd) would execute additional commands. This leads to remote code execution and potential full system compromise.
Recommendation: Avoid shell=True with user input. Use a list of arguments instead, which prevents shell interpretation of metacharacters.
| result = subprocess.run(f"echo {user_input}", shell=True, capture_output=True) | |
| result = subprocess.run(["echo", user_input], capture_output=True) |
| path = f"/data/{filename}" | ||
| with open(path, "r") as f: |
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🤖 Code Review Finding: Path traversal vulnerability allows reading arbitrary files
Severity: HIGH
Category: security
Impact: An attacker can read arbitrary files on the system by providing input like ../etc/passwd or ../../../../etc/shadow. This can expose sensitive configuration files, credentials, or application source code.
Recommendation: Validate and sanitize the filename. Use os.path.basename() to strip directory components, or resolve the full path and verify it remains within the intended directory.
| path = f"/data/{filename}" | |
| with open(path, "r") as f: | |
| safe_filename = os.path.basename(filename) | |
| path = f"/data/{safe_filename}" | |
| with open(path, "r") as f: |
| query = f"SELECT * FROM users WHERE id = {user_id}" | ||
| return connection.execute(query) |
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🤖 Code Review Finding: SQL injection via string interpolation in query construction
Severity: HIGH
Category: security
Impact: An attacker can manipulate the query by providing malicious input like 1 OR 1=1 to bypass authentication, 1; DROP TABLE users-- to destroy data, or UNION SELECT attacks to exfiltrate data from other tables.
Recommendation: Use parameterized queries with placeholders. Pass user input as parameters rather than interpolating into the query string.
| query = f"SELECT * FROM users WHERE id = {user_id}" | |
| return connection.execute(query) | |
| query = "SELECT * FROM users WHERE id = ?" | |
| return connection.execute(query, (user_id,)) |
| def process_items(items): | ||
| """Process a list of items.""" | ||
| results = [] | ||
| for i in range(len(items)): |
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🤖 Code Review Finding: O(n²) algorithm with redundant self-comparisons
Severity: MEDIUM
Category: performance
Impact: For large lists, this will cause significant performance degradation. A list of 10,000 items requires 100 million comparisons. Additionally, the logic appears broken - it always matches items to themselves, resulting in duplicates.
Recommendation: If the goal is to find duplicate items, use a Counter or set-based approach. If finding unique items, use list(set(items)). The current implementation's intent is unclear.
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Summary
Test plan
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