EUVD-2026-58287

The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with th...

CVE-2026-72642 Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process

The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with th...

CVE-2026-72642 Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process

The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model, without validating that the offset stays within the bounds of the underlying storage. A user with th...

CVE-2026-73558

vLLM is an inference and serving engine for large language models. Prior to 0.27.0, an integer overflow in blockIdx.x 2 d in activationkernels.cu can cause actandmulkernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or...

CVE-2026-73558

The CVE concerns vLLM, an inference/serving engine for LLMs. Before version 0.27.0, an integer overflow in blockIdx.x * 2 * d within activation_kernels.cu enables act_and_mul_kernel to consume input from another user within the same inference batch, causing cross-user data leakage of partial or c...

CVE-2026-73558 vLLM: Cross-User Data Leak Vulnerability

vLLM is an inference and serving engine for large language models. Prior to 0.27.0, an integer overflow in blockIdx.x 2 d in activationkernels.cu can cause actandmulkernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or...

ai-threat-detection

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CVE-2026-14678

Buffer over-read in PostgreSQL pgtrgm index picksplit function reads past end of a heap buffer. This might allow a table maintainer to infer limited memory values, via the lossy signal of index split choices. Versions before PostgreSQL 18.6, 17.11, 16.15, 15.19, and 14.24 are affected...

CVE-2026-14678

Buffer over-read in PostgreSQL pgtrgm index picksplit function reads past end of a heap buffer. This might allow a table maintainer to infer limited memory values, via the lossy signal of index split choices. Versions before PostgreSQL 18.6, 17.11, 16.15, 15.19, and 14.24 are affected...

UBUNTU-CVE-2026-14678

Buffer over-read in PostgreSQL pgtrgm index picksplit function reads past end of a heap buffer. This might allow a table maintainer to infer limited memory values, via the lossy signal of index split choices. Versions before PostgreSQL 18.6, 17.11, 16.15, 15.19, and 14.24 are affected...

CVE-2026-14678

The provided records identify CVE-2026-14678 as a buffer over-read in PostgreSQL’s pg_trgm index picksplit function, which reads past the end of a heap buffer. This may allow a table maintainer to infer limited memory values via the lossy signal of index split choices. Affected are PostgreSQL ver...

Elasticsearch 8.19.20, 9.4.5, 9.5.1 Security Update (ESA-2026-123)

Use of Out-of-range Pointer Offset in the Elasticsearch Machine Learning Native Inference Process The native inference process that Elasticsearch uses to evaluate uploaded machine learning models accepts a model operation that computes a memory address from an offset supplied inside the model,...

CVE-2026-72629: Authorization Bypass Through User-Controlled Key

Authorization Bypass Through User-Controlled Key CWE-639 in Kibana can lead to unauthorized cross-space access via Accessing Functionality Not Properly Constrained by ACLs CAPEC-1. The result is disclosure of inference output from a trained model in a different space that the user is not authoriz...

PT-2026-71759

Name of the Vulnerable Software and Affected Versions Elasticsearch affected versions not specified Description The native inference process used to evaluate uploaded machine learning models fails to validate that a memory address offset supplied within the model remains within the bounds of the...

CVE-2026-73558: Integer Overflow or Wraparound

vLLM is an inference and serving engine for large language models. Prior to 0.27.0, an integer overflow in blockIdx.x 2 d in activationkernels.cu can cause actandmulkernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or...

PT-2026-71474

Name of the Vulnerable Software and Affected Versions PostgreSQL versions prior to 18.5 PostgreSQL versions prior to 17.11 PostgreSQL versions prior to 16.15 PostgreSQL versions prior to 15.19 PostgreSQL versions prior to 14.24 Description A buffer over-read occurs in the picksplit function of th...

CVE-2026-32788

Uncontrolled search path for some Approximate Bayesian Inference Framework before version on commit 484c949 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with a privileged user combined with a low complexity attack may enable escalation of...

CVE-2026-32788

Uncontrolled search path for some Approximate Bayesian Inference Framework before version on commit 484c949 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with a privileged user combined with a low complexity attack may enable escalation of...

EUVD-2026-56529

Uncontrolled search path for some Approximate Bayesian Inference Framework before version on commit 484c949 within Ring 3: User Applications may allow an escalation of privilege. Unprivileged software adversary with a privileged user combined with a low complexity attack may enable escalation of...

Once Poisoned, Arbitrarily Controlled: A Programmable Backdoor in VLMs

Existing vision-language model VLM backdoors are usually treated as static vulnerabilities: one-to-one and N-to-N attacks bind one or more triggers to a finite set of targets before victim training. This assumption substantially underestimates the threat. We show that a single poisoning phase can...