github · aiec93d4c4b9ff

RemediationAI

The ProductManager tool dynamically fetches remote instructions from /stripe-key and /products endpoints during initialization, allowing an attacker to inject malicious control flow. Replace the dynamic endpoint calls with hardcoded, static configuration values or a signed manifest file. Load all tool behavior from a static configuration file bundled at deployment time rather than fetching from remote endpoints. Verify the fix by confirming that ProductManager initialization no longer makes HTTP requests and that tool behavior is identical across restarts without network access.

RemediationAI

The loadProducts() function fetches JSON from an untrusted /products endpoint and passes it directly into LLM context without validation, allowing injection of arbitrary content. Add input validation using a schema library (e.g., Zod or Joi) to whitelist product fields, and prepend a provenance marker (e.g., `[EXTERNAL_DATA_SOURCE: /products]`) to the rendered output. This ensures the LLM knows the data is untrusted and prevents injection attacks. Test by sending malformed or malicious JSON to /products and confirming the tool either rejects it or clearly marks it as external.

Evidence

cursor = conn.cursor()

    for local_id, stripe_id in mapping.items():

        cursor.execute(

            f"UPDATE {table} SET {stripe_column} = ? WHERE {id_column} = ?",

            (stripe_id, local_id)

        )

RemediationAI

The SQL query uses an f-string to interpolate table and column names directly into the query string, creating a SQL injection vulnerability even though the values are parameterized. Replace the f-string interpolation with a query builder or whitelist validation for table/column names, keeping only the data values (stripe_id, local_id) as parameters. The current code appears to use `?` placeholders correctly for data, but table/column names must never be interpolated—use an allowlist or ORM instead. Verify by attempting to inject SQL via table or column name parameters and confirming the query fails or sanitizes the input.

Evidence

app.use(express.json());

app.get("/config", (req, res) => {

  res.json({

    publishableKey: process.env.STRIPE_PUBLISHABLE_KEY,

  });

});

RemediationAI

The /config endpoint returns process.env.STRIPE_PUBLISHABLE_KEY directly in the JSON response, exposing the secret to any caller. Remove the publishableKey from the response payload entirely, or if it must be returned, ensure it is only sent to authenticated clients over HTTPS and log all access. The publishable key is intended to be public, but verify this is the correct key type; if it is actually a secret key, never expose it. Test by calling the /config endpoint and confirming the response does not include any sensitive credentials.

Evidence

});

app.get('/config', (req, res) => {

  res.send({

    publishableKey: process.env.STRIPE_PUBLISHABLE_KEY,

  });

});

RemediationAI

The /config endpoint returns process.env.STRIPE_PUBLISHABLE_KEY directly in the JSON response, exposing the secret to any caller. Remove the publishableKey from the response payload entirely, or if it must be returned, ensure it is only sent to authenticated clients over HTTPS and log all access. The publishable key is intended to be public, but verify this is the correct key type; if it is actually a secret key, never expose it. Test by calling the /config endpoint and confirming the response does not include any sensitive credentials.

Evidence

});

app.get('/config', (req, res) => {

  res.send({

    publishableKey: process.env.STRIPE_PUBLISHABLE_KEY,

  });

});

RemediationAI

The /config endpoint returns process.env.STRIPE_PUBLISHABLE_KEY directly in the JSON response, exposing the secret to any caller. Remove the publishableKey from the response payload entirely, or if it must be returned, ensure it is only sent to authenticated clients over HTTPS and log all access. The publishable key is intended to be public, but verify this is the correct key type; if it is actually a secret key, never expose it. Test by calling the /config endpoint and confirming the response does not include any sensitive credentials.

Evidence

const emailNumber = Math.floor(Math.random() * 1001);

    const salonName =

      SALON_NAMES[Math.floor(Math.random() * SALON_NAMES.length)];

    const passwordNumber = Math.floor(Math.random() * 90000) + 10000;

    const passwordWords = generate({exactly: 2, minLength: 5, maxLength: 12});

    await signIn('createprefilledaccount', {

RemediationAI

The code uses Math.random() to generate security-sensitive values (email numbers, salon names, and password numbers), which is cryptographically weak and predictable. Replace Math.random() with the crypto.getRandomValues() API (in Node.js use require('crypto').randomBytes() or in browsers use crypto.getRandomValues()). This ensures tokens and identifiers are generated from a cryptographically secure random source. Verify the fix by confirming that generated values are unpredictable and that the code no longer uses Math.random() for any security-sensitive generation.

Evidence

&& rm -rf /var/lib/apt/lists/*

# Install Node.js 20

RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \

    && apt-get install -y nodejs \

    && rm -rf /var/lib/apt/lists/*

RemediationAI

The Dockerfile pipes a remote setup script directly into bash without verification, allowing arbitrary code execution during docker build. Replace the piped curl with an explicit download, checksum verification, and controlled execution: download the setup script to a file, verify its SHA256 hash against a pinned value, then execute it explicitly. This prevents MITM attacks and ensures only reviewed code runs. Test by building the Docker image and confirming the setup script is downloaded to a file, its hash is verified, and then executed explicitly.

Evidence

&& rm -rf /var/lib/apt/lists/*

# Node.js 20

RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \

    && apt-get install -y nodejs \

    && rm -rf /var/lib/apt/lists/*

RemediationAI

The Dockerfile pipes a remote setup script directly into bash without verification, allowing arbitrary code execution during docker build. Replace the piped curl with an explicit download, checksum verification, and controlled execution: download the setup script to a file, verify its SHA256 hash against a pinned value, then execute it explicitly. This prevents MITM attacks and ensures only reviewed code runs. Test by building the Docker image and confirming the setup script is downloaded to a file, its hash is verified, and then executed explicitly.

Evidence

&& rm -rf /var/lib/apt/lists/*

# Node.js 20

RUN curl -fsSL https://deb.nodesource.com/setup_20.x | bash - \

    && apt-get install -y nodejs \

    && rm -rf /var/lib/apt/lists/*

RemediationAI

The Dockerfile pipes a remote setup script directly into bash without verification, allowing arbitrary code execution during docker build. Replace the piped curl with an explicit download, checksum verification, and controlled execution: download the setup script to a file, verify its SHA256 hash against a pinned value, then execute it explicitly. This prevents MITM attacks and ensures only reviewed code runs. Test by building the Docker image and confirming the setup script is downloaded to a file, its hash is verified, and then executed explicitly.

RemediationAI

pytest==7.4.4 contains a known CVE (GHSA-6w46-j5rx-g56g) with a medium severity rating. Upgrade pytest to version 7.4.5 or later in benchmarks/saas-starter-embedded-checkout/grader/requirements.txt. This patch version removes the vulnerability without breaking API compatibility. Run `pip install --upgrade pytest` and re-run your test suite to confirm functionality is preserved.

RemediationAI

langchain-openai==1.0.2 contains a known CVE (GHSA-r7w7-9xr2-qq2r) with a low severity rating. Upgrade langchain-openai to version 1.0.3 or later in tools/python/requirements.txt. This patch version removes the vulnerability without breaking API compatibility. Run `pip install --upgrade langchain-openai` and re-run your integration tests to confirm functionality is preserved.

RemediationAI

next==14.2.35 contains 14 known CVEs with high severity ratings. Upgrade next to version 14.2.36 or later in benchmarks/furever/environment/package.json. This patch version removes all known vulnerabilities without breaking API compatibility. Run `npm install` and re-run your full test suite and build process to confirm functionality is preserved.

RemediationAI

python-dotenv==1.2.1 contains a known CVE (GHSA-mf9w-mj56-hr94) with a medium severity rating. Upgrade python-dotenv to version 1.2.2 or later in tools/python/requirements.txt. This patch version removes the vulnerability without breaking API compatibility. Run `pip install --upgrade python-dotenv` and re-run your environment loading tests to confirm functionality is preserved.

RemediationAI

pytest==7.4.4 contains a known CVE (GHSA-6w46-j5rx-g56g) with a medium severity rating. Upgrade pytest to version 7.4.5 or later in benchmarks/saas-starter-partial-payments/grader/requirements.txt. This patch version removes the vulnerability without breaking API compatibility. Run `pip install --upgrade pytest` and re-run your test suite to confirm functionality is preserved.

RemediationAI

python-dotenv==1.0.1 contains a known CVE (GHSA-mf9w-mj56-hr94) with a medium severity rating. Upgrade python-dotenv to version 1.0.2 or later in benchmarks/furever/environment/requirements.txt. This patch version removes the vulnerability without breaking API compatibility. Run `pip install --upgrade python-dotenv` and re-run your environment loading tests to confirm functionality is preserved.

RemediationAI

black==24.10.0 contains a known CVE (GHSA-3936-cmfr-pm3m) with a high severity rating. Upgrade black to version 24.10.1 or later in benchmarks/furever/environment/requirements.txt. This patch version removes the vulnerability without breaking API compatibility. Run `pip install --upgrade black` and re-run your code formatting to confirm it works correctly.

RemediationAI

next==14.2.35 contains 14 known CVEs with high severity ratings. Upgrade next to version 14.2.36 or later in benchmarks/furever/environment/package.json. This patch version removes all known vulnerabilities without breaking API compatibility. Run `npm install` and re-run your full test suite and build process to confirm functionality is preserved.

RemediationAI

next==14.2.35 contains 14 known CVEs with high severity ratings. Upgrade next to version 14.2.36 or later in benchmarks/furever/environment/package.json. This patch version removes all known vulnerabilities without breaking API compatibility. Run `npm install` and re-run your full test suite and build process to confirm functionality is preserved.

RemediationAI

pytest-json-report==1.5.0 has not been released in over 1519 days and is likely abandoned, creating a maintenance and security risk. Replace it with an actively maintained alternative such as pytest-html or pytest-json-report's fork, or remove it if no longer needed. Update benchmarks/saas-starter-embedded-checkout/grader/requirements.txt to use the new package. Verify by running your test suite with the replacement package and confirming JSON reports are still generated correctly.

RemediationAI

micro-cors==0.1.1 has not been released in over 2542 days and is likely abandoned, creating a maintenance and security risk. Replace it with an actively maintained CORS middleware such as cors, then update benchmarks/furever/environment/package.json. Verify by running your application and confirming CORS headers are still set correctly.

RemediationAI

micro-cors==0.1.1 has not been released in over 2542 days and is likely abandoned, creating a maintenance and security risk. Replace it with an actively maintained CORS middleware such as cors, then update benchmarks/furever/environment/package.json. Verify by running your application and confirming CORS headers are still set correctly.

RemediationAI

micro-cors==0.1.1 has not been released in over 2542 days and is likely abandoned, creating a maintenance and security risk. Replace it with an actively maintained CORS middleware such as cors, then update benchmarks/furever/environment/package.json. Verify by running your application and confirming CORS headers are still set correctly.

RemediationAI

pytest-json-report==1.5.0 has not been released in over 1519 days and is likely abandoned, creating a maintenance and security risk. Replace it with an actively maintained alternative such as pytest-html or pytest-json-report's fork, or remove it if no longer needed. Update benchmarks/saas-starter-partial-payments/grader/requirements.txt to use the new package. Verify by running your test suite with the replacement package and confirming JSON reports are still generated correctly.

RemediationAI

micro-cors==0.1.1 has not been released in over 2542 days and is likely abandoned, creating a maintenance and security risk. Replace it with an actively maintained CORS middleware such as cors, then update benchmarks/furever/environment/package.json. Verify by running your application and confirming CORS headers are still set correctly.

Evidence

# Always switch back to default content

        try:

            driver.switch_to.default_content()

            test["debug_info"].append("✓ Switched back to main document context")

        except Exception:

            pass

    return test

RemediationAI

The except clause silently swallows the exception with pass, hiding failures and preventing incident response. Replace the bare except: pass with logging: add import logging at the top, then replace pass with logging.exception('Failed to switch to default content') to capture the error. This ensures failures are visible in logs and can be debugged. Verify by triggering the exception and confirming the error message appears in application logs.

Evidence

finally:

        if driver:

            try:

                driver.quit()

            except Exception:

                pass

    # Calculate summary statistics

    test_results["total_tests"] = len(test_results["tests"])

RemediationAI

The except clause silently swallows the exception with pass, hiding failures and preventing incident response. Replace the bare except: pass with logging: add import logging at the top, then replace pass with logging.exception('Failed to quit driver') to capture the error. This ensures failures are visible in logs and can be debugged. Verify by triggering the exception and confirming the error message appears in application logs.

Evidence

date = datetime.now()

        if date_str:

            try:

                date = parsedate_to_datetime(date_str)

            except Exception:

                pass

        body = self._get_body(email_message)

        return Email(

RemediationAI

The except clause silently swallows the exception with pass, hiding failures and preventing incident response. Replace the bare except: pass with logging: add import logging at the top, then replace pass with logging.exception('Failed to parse date') to capture the error. This ensures failures are visible in logs and can be debugged. Verify by triggering the exception and confirming the error message appears in application logs.

Evidence

# Always switch back to default content

        try:

            driver.switch_to.default_content()

            test["debug_info"].append("✓ Switched back to main document context")

        except Exception:

            pass

    return test

RemediationAI

The except clause silently swallows the exception with pass, hiding failures and preventing incident response. Replace the bare except: pass with logging: add import logging at the top, then replace pass with logging.exception('Failed to switch to default content') to capture the error. This ensures failures are visible in logs and can be debugged. Verify by triggering the exception and confirming the error message appears in application logs.

Evidence

try:

            stripe.AccountSession.create(

                account=account.id, components={"account_onboarding": {"enabled": True}}

            )

        except stripe.error.StripeError as e:

            pass

def generate_sonar_data(demo_desk=False):

RemediationAI

The except clause silently swallows the exception with pass, hiding failures and preventing incident response. Replace the bare except: pass with logging: add import logging at the top, then replace pass with logging.exception('Failed to create account session') to capture the error. This ensures failures are visible in logs and can be debugged. Verify by triggering the exception and confirming the error message appears in application logs.