github · mcpHEAD

Evidence

except client.exceptions.NotAuthorizedException as e:

            logger.error(f'Authentication failed: {e}')

            logger.error('Please check your Cognito credentials (client ID, username, password)')

            logger.error(

                'Make sure the user exists in the Cognito User Pool and the password is correct'

            )

RemediationAI

The problem is that exception details from Cognito authentication failures are logged without redaction, potentially exposing sensitive information about credentials or user data. Replace the generic `logger.error(f'Authentication failed: {e}')` with a redacted version by wrapping the exception message using a redaction helper function (e.g., `redact_sensitive_data(str(e))`) or by logging only a generic error code without the exception detail. This fix prevents credential hints and internal error messages from being exposed in logs that may be aggregated or reviewed by unauthorized parties. Verify by triggering an authentication failure and confirming the log output contains no credential names, user details, or specific error context.

Evidence

tls_context.verify_mode = ssl.CERT_REQUIRED

                else:

                    tls_context.check_hostname = False

                    tls_context.verify_mode = ssl.CERT_NONE

                if tls_cert_path and tls_key_path:

                    tls_context.load_cert_chain(tls_cert_path, tls_key_path)

RemediationAI

The problem is that TLS certificate verification is disabled by setting `ssl.CERT_NONE` and `check_hostname = False`, allowing man-in-the-middle attacks to intercept and modify all traffic including credentials and tool responses. Remove the `else` branch that disables verification, or if a private CA is required, load the CA certificate bundle using `tls_context.load_verify_locations('/path/to/ca-bundle.pem')` instead of disabling verification entirely. This fix ensures all outbound connections validate the server's certificate against a trusted CA, preventing MITM interception. Verify by attempting a connection with an invalid certificate and confirming it is rejected; then test with the correct CA bundle to confirm legitimate connections succeed.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the OAuth/JWT-protected MCP server does not expose the mandatory `/.well-known/oauth-protected-resource` endpoint required by the June 2025 MCP authorization spec, preventing clients from discovering the authorization server configuration. Add a route handler (e.g., using Flask `@app.route('/.well-known/oauth-protected-resource')` or FastAPI `@app.get('/.well-known/oauth-protected-resource')`) that returns a JSON object with at minimum `{"resource": "<server-identifier>", "authorization_servers": ["<issuer-url-1>", "<issuer-url-2>"]}`. This fix enables compliant MCP clients to automatically discover and validate the authorization configuration, closing the spec compliance gap. Verify by making a GET request to `/.well-known/oauth-protected-resource` and confirming it returns valid JSON with the required fields.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the OAuth/JWT-protected MCP server does not expose the mandatory `/.well-known/oauth-protected-resource` endpoint required by the June 2025 MCP authorization spec, preventing clients from discovering the authorization server configuration. Add a route handler (e.g., using Flask `@app.route('/.well-known/oauth-protected-resource')` or FastAPI `@app.get('/.well-known/oauth-protected-resource')`) that returns a JSON object with at minimum `{"resource": "<server-identifier>", "authorization_servers": ["<issuer-url-1>", "<issuer-url-2>"]}`. This fix enables compliant MCP clients to automatically discover and validate the authorization configuration, closing the spec compliance gap. Verify by making a GET request to `/.well-known/oauth-protected-resource` and confirming it returns valid JSON with the required fields.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the OAuth/JWT-protected MCP server does not expose the mandatory `/.well-known/oauth-protected-resource` endpoint required by the June 2025 MCP authorization spec, preventing clients from discovering the authorization server configuration. Add a route handler (e.g., using Flask `@app.route('/.well-known/oauth-protected-resource')` or FastAPI `@app.get('/.well-known/oauth-protected-resource')`) that returns a JSON object with at minimum `{"resource": "<server-identifier>", "authorization_servers": ["<issuer-url-1>", "<issuer-url-2>"]}`. This fix enables compliant MCP clients to automatically discover and validate the authorization configuration, closing the spec compliance gap. Verify by making a GET request to `/.well-known/oauth-protected-resource` and confirming it returns valid JSON with the required fields.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the OAuth/JWT-protected MCP server does not expose the mandatory `/.well-known/oauth-protected-resource` endpoint required by the June 2025 MCP authorization spec, preventing clients from discovering the authorization server configuration. Add a route handler (e.g., using Flask `@app.route('/.well-known/oauth-protected-resource')` or FastAPI `@app.get('/.well-known/oauth-protected-resource')`) that returns a JSON object with at minimum `{"resource": "<server-identifier>", "authorization_servers": ["<issuer-url-1>", "<issuer-url-2>"]}`. This fix enables compliant MCP clients to automatically discover and validate the authorization configuration, closing the spec compliance gap. Verify by making a GET request to `/.well-known/oauth-protected-resource` and confirming it returns valid JSON with the required fields.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

AWS_REGION=us-east-1

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials in the README with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials in the README with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE  # pragma: allowlist secret

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY  # pragma: allowlist secret

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk  # pragma: allowlist secret

```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation with `# pragma: allowlist secret` comments, which suppresses secret scanning tools and creates a false sense of security if real credentials are later added. Remove the credentials and the allowlist pragma comments entirely, replacing them with placeholder text like `YOUR_AWS_ACCESS_KEY_ID`, and document that credentials must be loaded from environment variables or AWS credential files. This fix prevents the allowlist from being misused to hide real secrets and ensures secret scanning tools remain effective. Verify by removing the pragma comments and re-running your secret scanner to confirm it now flags any real credentials that might be present.

Evidence

```.env

  # contents of a .env file with fictitious AWS temporary credentials

  AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

  AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

  AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

  ```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the root README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

```.env

  # contents of a .env file with fictitious AWS temporary credentials

  AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

  AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

  AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

  ```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the docusaurus installation documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

class DynamoDBClientConfig:

    """Configuration for DynamoDB client setup."""

    DUMMY_ACCESS_KEY = 'AKIAIOSFODNN7EXAMPLE'  # pragma: allowlist secret

    DUMMY_SECRET_KEY = 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'  # pragma: allowlist secret

    DEFAULT_REGION = 'us-east-1'

RemediationAI

The problem is that hardcoded AWS access keys and secret keys are defined as class constants in `model_validation_utils.py`, even though they are marked as dummy values with a pragma allowlist comment. If real credentials are later added using the same pattern, the allowlist will suppress detection. Replace the hardcoded constants with environment variable lookups (e.g., `os.getenv('DUMMY_ACCESS_KEY', 'AKIAIOSFODNN7EXAMPLE')`) and remove the pragma allowlist comment to keep secret scanning active. This fix ensures that if real credentials are accidentally added, they will be detected by scanning tools. Verify by removing the pragma comment and re-running your secret scanner to confirm it flags the dummy credentials, then add a test that confirms the code reads from environment variables when present.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

```

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

```file

# fictitious `.env` file with AWS temporary credentials

AWS_ACCESS_KEY_ID=ASIAIOSFODNN7EXAMPLE

AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY

AWS_SESSION_TOKEN=AQoEXAMPLEH4aoAH0gNCAPy...truncated...zrkuWJOgQs8IZZaIv2BXIa2R4Olgk

AWS_REGION=us-east-1

RemediationAI

The problem is that fictitious AWS credentials are hardcoded in the README.md documentation, and if any real credentials were committed this way, anyone with repository access would have full AWS account access. Replace the example credentials with placeholder text like `YOUR_AWS_ACCESS_KEY_ID` and add a `.env.example` file showing the required environment variable names without values. This fix prevents accidental credential exposure and establishes a pattern where developers must explicitly load credentials from environment variables or a secrets manager at runtime. Verify by running `git log -p` or using a secret scanner tool to confirm no real AWS credentials exist in the repository history, and test that the application loads credentials from `AWS_ACCESS_KEY_ID` environment variable at startup.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the PostgreSQL MCP server performs destructive database operations (DELETE, DROP TABLE, etc.) without emitting any audit events, making it impossible to investigate who performed a destructive action and when. Add audit logging by calling a centralized audit function (e.g., `audit_log(action='DELETE', table=table_name, user=current_user, timestamp=datetime.now())`) immediately after each destructive operation succeeds, and log both the action parameters and the result. This fix creates an audit trail that enables post-incident investigation and compliance reporting. Verify by executing a destructive tool (e.g., deleting a record), then checking the audit log file or centralized logging system to confirm the action, user, timestamp, and affected resource are recorded.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the SAM local invoke tool performs destructive operations (subprocess execution, file deletion) without emitting any audit events, making it impossible to investigate who executed what code and when. Add audit logging by calling a centralized audit function (e.g., `audit_log(action='INVOKE', function=function_name, user=current_user, timestamp=datetime.now())`) immediately after each invocation completes, and log both the invocation parameters and the result. This fix creates an audit trail that enables post-incident investigation and compliance reporting. Verify by executing a SAM invoke tool, then checking the audit log file or centralized logging system to confirm the action, user, timestamp, and function name are recorded.

Evidence

# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express 

RemediationAI

The problem is that the document loader MCP server performs destructive operations (file deletion, document removal) without emitting any audit events, making it impossible to investigate who deleted what document and when. Add audit logging by calling a centralized audit function (e.g., `audit_log(action='DELETE', document_id=doc_id, user=current_user, timestamp=datetime.now())`) immediately after each destructive operation succeeds, and log both the operation parameters and the result. This fix creates an audit trail that enables post-incident investigation and compliance reporting. Verify by executing a destructive tool (e.g., deleting a document), then checking the audit log file or centralized logging system to confirm the action, user, timestamp, and affected resource are recorded.

Evidence

result = r.srem(key, member)

        return f"Successfully removed {result} member from set '{key}'"

    except ValkeyError as e:

        return f"Error removing from set '{key}': {str(e)}"

@mcp.tool()

RemediationAI

The problem is that the full ValkeyError exception message is returned directly to the caller in the `set.py` tool, exposing internal error details, library versions, and query structure that aid attackers in reconnaissance. Replace `return f"Error removing from set '{key}': {str(e)}"` with a generic error message like `return f"Error removing from set '{key}': operation failed"` and log the full exception details server-side using `logger.error(f'Valkey error: {e}', exc_info=True)`. This fix hides internal details from the client while preserving the full error context for debugging. Verify by triggering an error condition and confirming the client receives only a generic message, while the server logs contain the full exception traceback.

Evidence

result = r.hlen(key)

        return str(result)

    except ValkeyError as e:

        return f"Error getting hash length from '{key}': {str(e)}"

@mcp.tool()

RemediationAI

The problem is that the full ValkeyError exception message is returned directly to the caller in the `hash.py` tool, exposing internal error details, library versions, and query structure that aid attackers in reconnaissance. Replace `return f"Error getting hash length from '{key}': {str(e)}"` with a generic error message like `return f"Error getting hash length from '{key}': operation failed"` and log the full exception details server-side using `logger.error(f'Valkey error: {e}', exc_info=True)`. This fix hides internal details from the client while preserving the full error context for debugging. Verify by triggering an error condition and confirming the client receives only a generic message, while the server logs contain the full exception traceback.

Evidence

'expected': '< 80%',

                            '_key': f'cw_mem_high|{d["id"]}',

                        }

                    )

            except (ValueError, TypeError):

                pass

            if '🔴' in d['status_check']:

                findings.append(

                    {

RemediationAI

The problem is that the `except (ValueError, TypeError): pass` clause silently discards parsing errors with no logging or metrics, preventing incident responders from detecting data quality issues or malformed health check responses. Replace `pass` with `logger.warning(f'Failed to parse health metric for {d.get("id")}: {e}', exc_info=True)` and optionally increment a metric counter like `metrics.increment('health_parse_errors')`. This fix ensures all errors are visible for debugging and monitoring. Verify by triggering a ValueError or TypeError in the health check parsing and confirming a warning log entry and metric increment are recorded.

Evidence

$PORT = $match.Matches[0].Groups[1].Value

         break

       }

     } catch {}

   }

   if ($PORT) {

RemediationAI

The problem is that the PowerShell `catch {}` block silently discards all exceptions with no logging or error handling, preventing incident responders from detecting port discovery failures. Replace `catch {}` with `catch { Write-Error "Failed to find port: $_"; exit 1 }` or equivalent error logging. This fix ensures all errors are visible for debugging and monitoring. Verify by triggering an error condition (e.g., invalid registry access) and confirming an error message is logged.

Evidence

finally:

        if mock_file_path and os.path.exists(mock_file_path):

            try:

                os.unlink(mock_file_path)

            except OSError:

                pass

RemediationAI

The problem is that the `except OSError: pass` clause silently discards file deletion errors with no logging, preventing incident responders from detecting cleanup failures that could leave test artifacts behind. Replace `pass` with `logger.warning(f'Failed to clean up mock file {mock_file_path}: {e}')` to log the error. This fix ensures cleanup failures are visible for debugging and monitoring. Verify by making the file read-only or inaccessible, running the cleanup code, and confirming a warning log entry is recorded.

Evidence

if isinstance(cost, str) and '$' in cost:

                    try:

                        cost_value = float(cost.replace('$', '').replace(',', ''))

                        total_cost += cost_value

                    except ValueError:

                        pass

            if total_cost > 0:

                monthly_cost = f'${total_cost:.2f}'

RemediationAI

The problem is that the `except ValueError: pass` clause silently discards cost parsing errors with no logging, preventing incident responders from detecting malformed pricing data that could lead to incorrect cost calculations. Replace `pass` with `logger.warning(f'Failed to parse cost value "{cost}": {e}')` to log the error. This fix ensures parsing failures are visible for debugging and monitoring. Verify by passing a malformed cost string (e.g., `'invalid$price'`) and confirming a warning log entry is recorded.

Evidence

sample_{{ entity_name.lower() }}.{{ param }}

{%- if not loop.last %}, {% endif -%}

{%- endfor %})

            print(f"   🗑️  Deleted leftover {{ entity_name.lower() }} (if existed)")

        except Exception:

            pass  # Ignore errors - item might not exist

{%- endfor %}

{%- endfor %}

        print("✅ Pre-test cleanup completed\n")

RemediationAI

The problem is that the Jinja2 template contains `except Exception: pass` which silently discards all deletion errors with no logging, preventing incident responders from detecting cleanup failures in generated code. Replace `pass` with `logger.warning(f'Failed to delete leftover {{ entity_name.lower() }}: {e}')` or equivalent error logging in the generated code. This fix ensures cleanup failures are visible for debugging and monitoring. Verify by generating code from the template, running it with a deletion that fails (e.g., permission denied), and confirming a warning log entry is recorded.