CWE-338
Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG)
Monthly
Insecure PRNG import in the jahlives openssl_encrypt Python package (all versions before 1.4.0) places Python's non-cryptographic 'random' module (Mersenne Twister) into openssl_encrypt/modules/pqc.py, though the code makes no calls to it, so no cryptographic operation is presently weakened. VulnCheck reported this as a latent hazard: the import invites future maintainers to reach for random.randint() instead of secrets/os.urandom, which would yield predictable values recoverable from ~624 outputs. There is no public exploit identified at time of analysis and it is not on CISA KEV; the flaw was fixed by removing the import in version 1.4.0.
Cryptographically weak PRNG use in openssl_encrypt before 1.4.0 exposes steganographic pixel selection to full state-recovery attacks. The library's steganography module seeds Python's Mersenne Twister with only 64 bits of SHA-256 hash material derived from the user password, then uses it to select pixel positions for hidden data - a design flaw captured by CWE-338. Any attacker who knows or can brute-force the steganography password can deterministically reconstruct the PRNG sequence and enumerate every pixel carrying concealed data, breaking steganographic confidentiality entirely. No public exploit has been identified at time of analysis and no CISA KEV listing exists, but the MT state-recovery technique is well-documented and requires no novel research.
Predictable SM2 private-key and signing-nonce generation in the sm-crypto npm library (versions prior to 0.5.0) lets an attacker recover keys generated in Node.js. Because jsbn's SecureRandom checks window.crypto rather than Node's globalThis.crypto, the default sm2.generateKeyPairHex() falls back to seeding an ARC4 stream from Math.random() and the wall clock, so an adversary who observes a few Math.random() outputs and estimates the generation time can reconstruct private keys and forge signatures. Publicly available exploit code exists (reproduced end-to-end against the real npm packages), but no public exploit is identified as being used in active attacks and there is no CISA KEV listing.
Credential derivation in Red Hat OpenShift AI's Data Science Pipelines Operator allows unauthenticated remote attackers to predict MariaDB root/user passwords and MinIO access/secret keys by exploiting a cryptographically weak PRNG (CWE-338). Any attacker who can reach the MinIO Route or MariaDB Service over the network can compute valid credentials without prior authentication, then access all pipeline artifacts and metadata stored in the system. No active exploitation is confirmed and no public exploit code has been identified at time of analysis, but the CVSS 7.5 High rating with AV:N and PR:N reflects a no-authentication-barrier condition against exposed services.
Predictable Fernet encryption key generation in IBM Langflow OSS (versions 1.0.0 through 1.10.3) stems from the ensure_fernet_key() function deriving keys from a cryptographically weak source (CWE-338), letting an attacker reconstruct the symmetric key that protects stored secrets such as API keys, credentials, and connection variables. Because the key is guessable rather than random, an actor who can read Langflow's encrypted data store can decrypt it and, depending on how the key is reused, potentially forge or tamper with protected values. There is no public exploit identified at time of analysis and EPSS is low (0.21%, 11th percentile), but IBM rates the flaw CVSS 9.8 and a vendor patch is available.
Predictable shared-secret generation in Spring Tools for Eclipse (5.2.0 and earlier) weakens the authentication protecting Spring Boot DevTools remote-restart uploads to remotely deployed targets such as Docker containers or Cloud Foundry apps. Because the secret is derived from a non-cryptographic PRNG rather than a secure random source, an attacker able to reach the deployed application's DevTools remote endpoint may predict the secret, authenticate restart uploads, and push arbitrary code, yielding remote code execution (CVSS 8.3, scope-changed). There is no public exploit identified at time of analysis, and it is not listed in CISA KEV.
Predictable persistent login keys in IP Login extension for Joomla allow unauthenticated attackers to compromise accounts. Versions 1.0.0 through 6.2.5 generate keys using a non-cryptographic random generator with insufficient entropy, enabling brute-force guessing. No public exploit is known, but the low-entropy PRNG makes key prediction feasible.
PKCE bypass in librest allows OAuth 2.0 client impersonation by exploiting a cryptographically weak pseudo-random number generator. The library's code verifier generation for PKCE (RFC 7636) uses GLib's GRand function - a non-cryptographic PRNG with a 32-bit seed space - rather than a CSPRNG, enabling a positioned attacker to recover the PRNG seed and reconstruct the code verifier to hijack the token exchange. Rated CVSS 6.8 with high confidentiality and integrity impact; no public exploit or CISA KEV listing identified at time of analysis.
Privilege escalation in Extreme Networks ExtremeXOS (EXOS / Switch Engine) lets an attacker reach the privileged debug-mode function because its challenge-response authorization derives the challenge from an insufficiently random source (CWE-338), making the expected response predictable under certain conditions. An attacker holding a valid low-privilege account (remote) or physical serial console access (local) can bypass debug-mode authorization and, depending on device model and firmware, escalate to root and persistently modify the device software stack. There is no public exploit identified at time of analysis and the CVE is not listed in CISA KEV; the vendor (Extreme Networks) self-reported the issue.
Predictable session ID generation in the Dancer2 Perl web framework (all versions through 2.1.0) allows remote attackers to guess valid session identifiers and hijack user sessions. When both the Math::Random::ISAAC::XS and Crypt::URandom CSPRNG modules are absent, Dancer2::Core::Role::SessionFactory::generate_id silently falls back to a SHA-1 hash of low-entropy, largely deterministic inputs (built-in rand(), module path, a counter, PID, an object memory address, and a rand-shuffled string). There is no public exploit identified at time of analysis, KEV lists no active exploitation, and EPSS is low (0.14%), but the flaw is rated CVSS 8.2 and CISA SSVC marks it automatable with partial technical impact.
Insecure PRNG import in the jahlives openssl_encrypt Python package (all versions before 1.4.0) places Python's non-cryptographic 'random' module (Mersenne Twister) into openssl_encrypt/modules/pqc.py, though the code makes no calls to it, so no cryptographic operation is presently weakened. VulnCheck reported this as a latent hazard: the import invites future maintainers to reach for random.randint() instead of secrets/os.urandom, which would yield predictable values recoverable from ~624 outputs. There is no public exploit identified at time of analysis and it is not on CISA KEV; the flaw was fixed by removing the import in version 1.4.0.
Cryptographically weak PRNG use in openssl_encrypt before 1.4.0 exposes steganographic pixel selection to full state-recovery attacks. The library's steganography module seeds Python's Mersenne Twister with only 64 bits of SHA-256 hash material derived from the user password, then uses it to select pixel positions for hidden data - a design flaw captured by CWE-338. Any attacker who knows or can brute-force the steganography password can deterministically reconstruct the PRNG sequence and enumerate every pixel carrying concealed data, breaking steganographic confidentiality entirely. No public exploit has been identified at time of analysis and no CISA KEV listing exists, but the MT state-recovery technique is well-documented and requires no novel research.
Predictable SM2 private-key and signing-nonce generation in the sm-crypto npm library (versions prior to 0.5.0) lets an attacker recover keys generated in Node.js. Because jsbn's SecureRandom checks window.crypto rather than Node's globalThis.crypto, the default sm2.generateKeyPairHex() falls back to seeding an ARC4 stream from Math.random() and the wall clock, so an adversary who observes a few Math.random() outputs and estimates the generation time can reconstruct private keys and forge signatures. Publicly available exploit code exists (reproduced end-to-end against the real npm packages), but no public exploit is identified as being used in active attacks and there is no CISA KEV listing.
Credential derivation in Red Hat OpenShift AI's Data Science Pipelines Operator allows unauthenticated remote attackers to predict MariaDB root/user passwords and MinIO access/secret keys by exploiting a cryptographically weak PRNG (CWE-338). Any attacker who can reach the MinIO Route or MariaDB Service over the network can compute valid credentials without prior authentication, then access all pipeline artifacts and metadata stored in the system. No active exploitation is confirmed and no public exploit code has been identified at time of analysis, but the CVSS 7.5 High rating with AV:N and PR:N reflects a no-authentication-barrier condition against exposed services.
Predictable Fernet encryption key generation in IBM Langflow OSS (versions 1.0.0 through 1.10.3) stems from the ensure_fernet_key() function deriving keys from a cryptographically weak source (CWE-338), letting an attacker reconstruct the symmetric key that protects stored secrets such as API keys, credentials, and connection variables. Because the key is guessable rather than random, an actor who can read Langflow's encrypted data store can decrypt it and, depending on how the key is reused, potentially forge or tamper with protected values. There is no public exploit identified at time of analysis and EPSS is low (0.21%, 11th percentile), but IBM rates the flaw CVSS 9.8 and a vendor patch is available.
Predictable shared-secret generation in Spring Tools for Eclipse (5.2.0 and earlier) weakens the authentication protecting Spring Boot DevTools remote-restart uploads to remotely deployed targets such as Docker containers or Cloud Foundry apps. Because the secret is derived from a non-cryptographic PRNG rather than a secure random source, an attacker able to reach the deployed application's DevTools remote endpoint may predict the secret, authenticate restart uploads, and push arbitrary code, yielding remote code execution (CVSS 8.3, scope-changed). There is no public exploit identified at time of analysis, and it is not listed in CISA KEV.
Predictable persistent login keys in IP Login extension for Joomla allow unauthenticated attackers to compromise accounts. Versions 1.0.0 through 6.2.5 generate keys using a non-cryptographic random generator with insufficient entropy, enabling brute-force guessing. No public exploit is known, but the low-entropy PRNG makes key prediction feasible.
PKCE bypass in librest allows OAuth 2.0 client impersonation by exploiting a cryptographically weak pseudo-random number generator. The library's code verifier generation for PKCE (RFC 7636) uses GLib's GRand function - a non-cryptographic PRNG with a 32-bit seed space - rather than a CSPRNG, enabling a positioned attacker to recover the PRNG seed and reconstruct the code verifier to hijack the token exchange. Rated CVSS 6.8 with high confidentiality and integrity impact; no public exploit or CISA KEV listing identified at time of analysis.
Privilege escalation in Extreme Networks ExtremeXOS (EXOS / Switch Engine) lets an attacker reach the privileged debug-mode function because its challenge-response authorization derives the challenge from an insufficiently random source (CWE-338), making the expected response predictable under certain conditions. An attacker holding a valid low-privilege account (remote) or physical serial console access (local) can bypass debug-mode authorization and, depending on device model and firmware, escalate to root and persistently modify the device software stack. There is no public exploit identified at time of analysis and the CVE is not listed in CISA KEV; the vendor (Extreme Networks) self-reported the issue.
Predictable session ID generation in the Dancer2 Perl web framework (all versions through 2.1.0) allows remote attackers to guess valid session identifiers and hijack user sessions. When both the Math::Random::ISAAC::XS and Crypt::URandom CSPRNG modules are absent, Dancer2::Core::Role::SessionFactory::generate_id silently falls back to a SHA-1 hash of low-entropy, largely deterministic inputs (built-in rand(), module path, a counter, PID, an object memory address, and a rand-shuffled string). There is no public exploit identified at time of analysis, KEV lists no active exploitation, and EPSS is low (0.14%), but the flaw is rated CVSS 8.2 and CISA SSVC marks it automatable with partial technical impact.