#dos

The Alt Redirect 1.6.3 addon for Statamic fails to consistently strip query string parameters when the "Query String Strip" feature is enabled. Case variations, encoded keys, and duplicates are not removed, allowing attackers to bypass sanitization. This may lead to cache poisoning, parameter pollution, or denial of service.

rardecode versions <= 2.1.1 fail to restrict the dictionary size when reading large RAR dictionary sizes, which allows an attacker to provide a specially crafted RAR file and cause Denial of Service via an Out Of Memory Crash.

### Summary Amazon.IonDotnet is a library for the Dotnet language that is used to read and write Amazon Ion data. An issue exists where, under certain circumstances, the library could an infinite loop, resulting in denial of service. As of August 20, 2025, this library has been deprecated and will not receive further updates. ### Impact An infinite loop issue in Amazon.IonDotnet library versions <v1.3.2 may allow a threat actor to cause a denial of service through a specially crafted text input. This invalid input triggered an error condition in the parser that was handled improperly, resulting in an infinite loop. ### Impacted versions: <1.3.2 ### Patches This issue has been addressed in Amazon.IonDotnet version [1.3.2](https://www.nuget.org/packages/Amazon.IonDotnet/1.3.2). We recommend upgrading to the latest version and ensuring any forked or derivative code is patched to incorporate the new fixes. ### Workarounds Only accept data from trusted sources, written using a supported...

View CSAF 1. EXECUTIVE SUMMARY CVSS v4 6.3 ATTENTION: Exploitable remotely/Low attack complexity Vendor: Rockwell Automation Equipment: Industrial Data Center (IDC) with Cisco Switching, IDC-Managed Support contract with Cisco Switching, Network-Managed Support contract with Cisco network switch, Firewall-Managed Support contract with Cisco firewall Vulnerability: Stack-based Buffer Overflow 2. RISK EVALUATION Successful exploitation of this vulnerability could result in arbitrary code execution. 3. TECHNICAL DETAILS 3.1 AFFECTED PRODUCTS Rockwell Automation reports the following Lifecycle Services with Cisco are affected: Industrial Data Center (IDC) with Cisco Switching: Generations 1 - 5 IDC-Managed Support contract with Cisco Switching: Generations 1 - 5 Network-Managed Support contract with Cisco network switch: All versions Firewall-Managed Support contract with Cisco firewall: All versions 3.2 VULNERABILITY OVERVIEW 3.2.1 STACK-BASED BUFFER OVERFLOW CWE-121 A third-party vulnera...

View CSAF 1. EXECUTIVE SUMMARY CVSS v4 6.3 ATTENTION: Exploitable remotely/Low attack complexity Vendor: Rockwell Automation Equipment: Stratix 5700, 5400, 5410, 5200, 5800 Vulnerability: Stack-based Buffer Overflow 2. RISK EVALUATION Successful exploitation of this vulnerability could result in arbitrary code execution. 3. TECHNICAL DETAILS 3.1 AFFECTED PRODUCTS The following version of Stratix 5700 is affected: Stratix 5700: Version v15.2(8)E7 and prior Stratix 5400: Version v15.2(8)E7 and prior Stratix 5410: Version v15.2(8)E7 and prior Stratix 5200: Version v17.17.01 and prior Stratix 5800: Version v17.17.01 and prior 3.2 VULNERABILITY OVERVIEW 3.2.1 STACK-BASED BUFFER OVERFLOW CWE-121 A third-party vulnerability exists in the affected products. The affected products use Cisco IOS XE Software which contains a vulnerability in the Simple Network Management Protocol (SNMP) subsystem. An authenticated, remote attacker with low privileges could cause a denial-of-Service (DoS) condition...

You’ve trained the model, packaged it on Red Hat OpenShift AI, and it’s ready to work. The next move is exposing it through an API so people and applications can use it. At that moment, your model stops being an internal experiment and becomes a front-door service. And like any front door, somebody is going to knock … sometimes it’s the right user, sometimes not.Your model is no longer just a project in a lab: it’s a production endpoint. And like any endpoint, it’s a target. How do you ensure that only the right applications and users are interacting with it? How do you protect the

### Summary A Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The `load_from_url` and `load_from_url_async` methods fetch and process media from user-provided URLs without adequate restrictions on the target hosts. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources. This vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. ### Vulnerability Details The core of the vulnerability lies in the `MediaConnector.load_from_url` method and its asynchronous ...

### Summary A resource-exhaustion (denial-of-service) vulnerability exists in multiple endpoints of the OpenAI-Compatible Server due to the ability to specify Jinja templates via the `chat_template` and `chat_template_kwargs` parameters. If an attacker can supply these parameters to the API, they can cause a service outage by exhausting CPU and/or memory resources. ### Details When using an LLM as a chat model, the conversation history must be rendered into a text input for the model. In `hf/transformer`, this rendering is performed using a Jinja template. The OpenAI-Compatible Server launched by vllm serve exposes a `chat_template` parameter that lets users specify that template. In addition, the server accepts a `chat_template_kwargs` parameter to pass extra keyword arguments to the rendering function. Because Jinja templates support programming-language-like constructs (loops, nested iterations, etc.), a crafted template can consume extremely large amounts of CPU and memory and ...

## Summary `Rack::Multipart::Parser` can accumulate unbounded data when a multipart part’s header block never terminates with the required blank line (`CRLFCRLF`). The parser keeps appending incoming bytes to memory without a size cap, allowing a remote attacker to exhaust memory and cause a denial of service (DoS). ## Details While reading multipart headers, the parser waits for `CRLFCRLF` using: ```ruby @sbuf.scan_until(/(.*?\r\n)\r\n/m) ``` If the terminator never appears, it continues appending data (`@sbuf.concat(content)`) indefinitely. There is no limit on accumulated header bytes, so a single malformed part can consume memory proportional to the request body size. ## Impact Attackers can send incomplete multipart headers to trigger high memory use, leading to process termination (OOM) or severe slowdown. The effect scales with request size limits and concurrency. All applications handling multipart uploads may be affected. ## Mitigation * Upgrade to a patched Rack vers...

## Summary `Rack::Multipart::Parser` stores non-file form fields (parts without a `filename`) entirely in memory as Ruby `String` objects. A single large text field in a multipart/form-data request (hundreds of megabytes or more) can consume equivalent process memory, potentially leading to out-of-memory (OOM) conditions and denial of service (DoS). ## Details During multipart parsing, file parts are streamed to temporary files, but non-file parts are buffered into memory: ```ruby body = String.new # non-file → in-RAM buffer @mime_parts[mime_index].body << content ``` There is no size limit on these in-memory buffers. As a result, any large text field—while technically valid—will be loaded fully into process memory before being added to `params`. ## Impact Attackers can send large non-file fields to trigger excessive memory usage. Impact scales with request size and concurrency, potentially leading to worker crashes or severe garbage-collection overhead. All Rack applications p...