Numpy
Monthly
Sandbox escape in asteval (the lmfit restricted Python expression evaluator) before 1.0.9 lets an attacker-controlled expression turn numpy's ndarray.ctypes wrapper into an arbitrary process-memory read/write primitive, which is equivalent to code execution and can also leak secrets or crash the host process. Exploitation requires the DEFAULT Interpreter with numpy enabled (the default whenever numpy is installed) and a host application that evaluates untrusted expression strings; per the assessed vector it is unauthenticated (PR:N) with no user interaction and needs no import, __dunder__ access, eval, or getattr. The headline is a sharp signal conflict: NVD-style scoring gives only 5.5 (AV:L/AC:L/PR:N/UI:R, availability-only), while the independent assessment scores the same flaw CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H, so real-world risk depends entirely on how the consuming application exposes the evaluator. No public exploit code was identified at time of analysis, and the issue is fully remediated by upgrading to 1.0.9.
Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative. Rated medium severity (CVSS 5.5), this vulnerability is low attack complexity. Public exploit code available and no vendor patch available.
Null Pointer Dereference vulnerability exists in numpy.sort in NumPy < and 1.19 in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable. Public exploit code available and no vendor patch available.
An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable. Public exploit code available and no vendor patch available.
An issue was discovered in NumPy before 1.16.3. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
The numpy.pad function in Numpy 1.13.1 and older versions is missing input validation. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
Sandbox escape in asteval (the lmfit restricted Python expression evaluator) before 1.0.9 lets an attacker-controlled expression turn numpy's ndarray.ctypes wrapper into an arbitrary process-memory read/write primitive, which is equivalent to code execution and can also leak secrets or crash the host process. Exploitation requires the DEFAULT Interpreter with numpy enabled (the default whenever numpy is installed) and a host application that evaluates untrusted expression strings; per the assessed vector it is unauthenticated (PR:N) with no user interaction and needs no import, __dunder__ access, eval, or getattr. The headline is a sharp signal conflict: NVD-style scoring gives only 5.5 (AV:L/AC:L/PR:N/UI:R, availability-only), while the independent assessment scores the same flaw CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H, so real-world risk depends entirely on how the consuming application exposes the evaluator. No public exploit code was identified at time of analysis, and the issue is fully remediated by upgrading to 1.0.9.
Buffer overflow in the array_from_pyobj function of fortranobject.c in NumPy < 1.19, which allows attackers to conduct a Denial of Service attacks by carefully constructing an array with negative. Rated medium severity (CVSS 5.5), this vulnerability is low attack complexity. Public exploit code available and no vendor patch available.
Null Pointer Dereference vulnerability exists in numpy.sort in NumPy < and 1.19 in the PyArray_DescrNew function due to missing return-value validation, which allows attackers to conduct DoS. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable. Public exploit code available and no vendor patch available.
An incomplete string comparison in the numpy.core component in NumPy before 1.22.0 allows attackers to trigger slightly incorrect copying by constructing specific string objects. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.
A Buffer Overflow vulnerability exists in NumPy 1.9.x in the PyArray_NewFromDescr_int function of ctors.c when specifying arrays of large dimensions (over 32) from Python code, which could let a. Rated medium severity (CVSS 5.3), this vulnerability is remotely exploitable. Public exploit code available and no vendor patch available.
An issue was discovered in NumPy before 1.16.3. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and no vendor patch available.
The numpy.pad function in Numpy 1.13.1 and older versions is missing input validation. Rated high severity (CVSS 7.5), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available.