When a product allows a user's input to contain code syntax, it might be possible for an attacker to craft the code in such a way that it will alter the intended control flow of the product. Such an alteration could lead to arbitrary code execution.
Injection problems encompass a wide variety of issues -- all mitigated in very different ways. For this reason, the most effective way to discuss these weaknesses is to note the distinct features which classify them as injection weaknesses. The most important issue to note is that all injection problems share one thing in common -- i.e., they allow for the injection of control plane data into the user-controlled data plane. This means that the execution of the process may be altered by sending code in through legitimate data channels, using no other mechanism. While buffer overflows, and many other flaws, involve the use of some further issue to gain execution, injection problems need only for the data to be parsed. The most classic instantiations of this category of weakness are SQL injection and format string vulnerabilities.
Scope | Impact | Likelihood |
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Access Control | Bypass Protection Mechanism Note: In some cases, injectable code controls authentication; this may lead to a remote vulnerability. | |
Access Control | Gain Privileges or Assume Identity Note: Injected code can access resources that the attacker is directly prevented from accessing. | |
Integrity Confidentiality Availability | Execute Unauthorized Code or Commands Note: Code injection attacks can lead to loss of data integrity in nearly all cases as the control-plane data injected is always incidental to data recall or writing. Additionally, code injection can often result in the execution of arbitrary code. | |
Non-Repudiation | Hide Activities Note: Often the actions performed by injected control code are unlogged. |
Reference | Description |
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Math component in an LLM framework translates user input into a Python expression that is input into the Python exec() method, allowing code execution - one variant of a "prompt injection" attack. | |
Python-based library uses an LLM prompt containing user input to dynamically generate code that is then fed as input into the Python exec() method, allowing code execution - one variant of a "prompt injection" attack. | |
Framework for LLM applications allows eval injection via a crafted response from a hosting provider. | |
Python compiler uses eval() to execute malicious strings as Python code. | |
Chain: regex in EXIF processor code does not correctly determine where a string ends (CWE-625), enabling eval injection (CWE-95), as exploited in the wild per CISA KEV. | |
"Code injection" in VPN product, as exploited in the wild per CISA KEV. | |
Eval injection in PHP program. | |
Eval injection in Perl program. | |
Eval injection in Perl program using an ID that should only contain hyphens and numbers. | |
Direct code injection into Perl eval function. | |
Eval injection in Perl program. | |
Direct code injection into Perl eval function. | |
Direct code injection into Perl eval function. | |
MFV. code injection into PHP eval statement using nested constructs that should not be nested. | |
MFV. code injection into PHP eval statement using nested constructs that should not be nested. | |
Code injection into Python eval statement from a field in a formatted file. | |
Eval injection in Python program. | |
chain: Resultant eval injection. An invalid value prevents initialization of variables, which can be modified by attacker and later injected into PHP eval statement. | |
Perl code directly injected into CGI library file from parameters to another CGI program. | |
Direct PHP code injection into supporting template file. | |
Direct code injection into PHP script that can be accessed by attacker. | |
PHP code from User-Agent HTTP header directly inserted into log file implemented as PHP script. |
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].
CAPEC-ID | Attack Pattern Name |
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CAPEC-242 | Code Injection An adversary exploits a weakness in input validation on the target to inject new code into that which is currently executing. This differs from code inclusion in that code inclusion involves the addition or replacement of a reference to a code file, which is subsequently loaded by the target and used as part of the code of some application. |
CAPEC-35 | Leverage Executable Code in Non-Executable Files An attack of this type exploits a system's trust in configuration and resource files. When the executable loads the resource (such as an image file or configuration file) the attacker has modified the file to either execute malicious code directly or manipulate the target process (e.g. application server) to execute based on the malicious configuration parameters. Since systems are increasingly interrelated mashing up resources from local and remote sources the possibility of this attack occurring is high. |
CAPEC-77 | Manipulating User-Controlled Variables This attack targets user controlled variables (DEBUG=1, PHP Globals, and So Forth). An adversary can override variables leveraging user-supplied, untrusted query variables directly used on the application server without any data sanitization. In extreme cases, the adversary can change variables controlling the business logic of the application. For instance, in languages like PHP, a number of poorly set default configurations may allow the user to override variables. |
Name | Organization | Date | Date Release | Version |
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PLOVER | Draft 3 |
Name | Organization | Date | Comment |
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Eric Dalci | Cigital | updated Time_of_Introduction | |
CWE Content Team | MITRE | updated Applicable_Platforms, Relationships, Research_Gaps, Taxonomy_Mappings | |
CWE Content Team | MITRE | updated Common_Consequences, Demonstrative_Examples, Description, Likelihood_of_Exploit, Name, Potential_Mitigations, Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Name | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Description, Potential_Mitigations | |
CWE Content Team | MITRE | updated Name | |
CWE Content Team | MITRE | updated Common_Consequences | |
CWE Content Team | MITRE | updated Common_Consequences, Demonstrative_Examples, Observed_Examples, References, Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Modes_of_Introduction, Relationships | |
CWE Content Team | MITRE | updated Related_Attack_Patterns, Type | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations, Relationships | |
CWE Content Team | MITRE | updated Potential_Mitigations | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Demonstrative_Examples | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Relationships | |
CWE Content Team | MITRE | updated Research_Gaps | |
CWE Content Team | MITRE | updated Observed_Examples, Relationships | |
CWE Content Team | MITRE | updated Observed_Examples | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Description, Potential_Mitigations, Relationships | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Detection_Factors, Relationships, Time_of_Introduction | |
CWE Content Team | MITRE | updated Mapping_Notes, Relationships, Taxonomy_Mappings | |
CWE Content Team | MITRE | updated Demonstrative_Examples, Potential_Mitigations, References | |
CWE Content Team | MITRE | updated Applicable_Platforms, Observed_Examples | |
CWE Content Team | MITRE | updated Mapping_Notes, Relationships |