This High severity Injection vulnerability was introduced in Assets Discovery 1.0 - 6.2.0 (all versions).
Assets Discovery, which can be downloaded via Atlassian Marketplace, is a network scanning tool that can be used with or without an agent with Jira Service Management Cloud, Data Center or Server. It detects hardware and software that is connected to your local network and extracts detailed information about each asset. This data can then be imported into Assets in Jira Service Management to help you manage all of the devices and configuration items within your local network.
This Injection vulnerability, with a CVSS Score of 7.2, allows an authenticated attacker to modify the actions taken by a system call which has high impact to confidentiality, high impact to integrity, high impact to availability, and requires no user interaction.
Atlassian recommends that Assets Discovery customers upgrade to latest version, if you are unable to do so, upgrade your instance to one of the specified supported fixed versions
See the release notes (https://confluence.atlassian.com/assetapps/assets-discovery-3-2-1-cloud-6-2-1-data_center-1333987182.html). You can download the latest version of Assets Discovery from the Atlassian Marketplace (https://marketplace.atlassian.com/apps/1214668/assets-discovery?hosting=datacenter&tab=installation).
This vulnerability was reported via our Penetration Testing program.
Weakness
The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.
Affected Software
| Name |
Vendor |
Start Version |
End Version |
| Assets_discovery_data_center |
Atlassian |
1.0.0 (including) |
6.2.1 (excluding) |
Potential Mitigations
- Run your code in a “jail” or similar sandbox environment that enforces strict boundaries between the process and the operating system. This may effectively restrict which code can be executed by your product.
- Examples include the Unix chroot jail and AppArmor. In general, managed code may provide some protection.
- This may not be a feasible solution, and it only limits the impact to the operating system; the rest of your application may still be subject to compromise.
- Be careful to avoid CWE-243 and other weaknesses related to jails.
- Assume all input is malicious. Use an “accept known good” input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
- When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, “boat” may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as “red” or “blue.”
- Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code’s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- To reduce the likelihood of code injection, use stringent allowlists that limit which constructs are allowed. If you are dynamically constructing code that invokes a function, then verifying that the input is alphanumeric might be insufficient. An attacker might still be able to reference a dangerous function that you did not intend to allow, such as system(), exec(), or exit().
- For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
References