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compliance-trestle Vulnerable to Remote Code Execution via Recursive Server-Side Template Injection (SSTI)

compliance-trestle is a tooling platform for managing compliance as code. Versions prior to 3.12.2 and 4.0.3 have a Server-Side Template Injection (SSTI) vulnerability exists in the `trestle author jinja` command. The command recursively evaluates rendered templates, allowing an attacker to achieve arbitrary command execution with privileges of the running process by injecting malicious payloads into data fields (such as SSP documents or Lookup Tables). The vulnerability does not require attacker control of the template itself. Only attacker-controlled input data rendered into a trusted template is required. This distinction is critical: the

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UBUNTU-CVE-2026-12876

[Unknown description]

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JupyterLab: Cross-site scripting (XSS) via crafted settings file (`overrides.json`)

jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 3.3.0 until 4.5.10 and 4.6.2, JupyterLab allows notebook settings to be shared and applied through an overrides.json file using the Import button in the Settings Editor. In packages/notebook-extension/schema/tracker.json and packages/notebook-extension/src/index.ts, the sideBySideLeftMarginOverride and sideBySideRightMarginOverride settings are not properly validated before being inserted into style content, allowing a crafted settings file to contain instructions that execute as code instead of only changing di

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jupyterlab: PyPI extension blocklist package-name canonicalization bypass

jupyterlab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook Architecture. From 4.5.0 until 4.5.10 and 4.6.2, in jupyterlab/extensions/manager.py and jupyterlab/extensions/pypi.py, JupyterLab's PyPI extension manager enforces blocked_extensions_uris by comparing requested install names to blocklist entries with custom normalization that is weaker than PyPI package-name canonicalization. An authenticated user can request a PyPI-equivalent spelling such as JupyterLab.Git for a blocklisted package such as jupyterlab-git, and JupyterLab accepts the install request even though pip resolves the v

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compliance-trestle Profile Import has an Arbitrary File Read via trestle:// URI and Relative Path Traversal

compliance-trestle is a tooling platform for managing compliance as code. Prior to versions 3.12.2 and 4.0.3, the compliance-trestle library's profile import mechanism resolves `trestle://` URIs and relative file paths by joining them with `trestle_root` and calling `.resolve()`, but performs no boundary check to ensure the resolved path stays within the trestle workspace. An attacker can craft a malicious OSCAL profile YAML with `imports[].href` containing path traversal sequences to read arbitrary files from the server filesystem. Versions 3.12.3 and 4.0.3 patch the issue.

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compliance-trestle Remote Fetching Mechanism has an Arbitrary File Write via Cache Path Traversal

compliance-trestle is a tooling platform for managing compliance as code. Prior to versiions 3.12.2 and 4.0.3, the compliance-trestle library's remote fetching cache mechanism (HTTPSFetcher and SFTPFetcher) constructs the local cache file path from the URL path component without sanitizing path traversal sequences (`../`). When a remote OSCAL profile references a URL with traversal in its path, the HTTP response body is written to a location outside the intended cache directory, enabling arbitrary file write with attacker-controlled content to the filesystem. Versions 3.12.3 and 4.0.3 patch the issue.

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WsgiDAV encoded dot segments can escape filesystem share roots

WsgiDAV is a generic and extendable WebDAV server based on WSGI. WsgiDAV 4.3.3 and prior can allow a WebDAV request path containing an encoded parent-directory segment to escape the configured filesystem share root in a specific path layout. The issue is fixed with version 4.3.4.

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Open WebUI: Cross-channel message overwrite via chat completion API (single-model and multimodel message_ids)

Open WebUI is an extensible, feature-rich, and user-friendly self-hosted AI platform. From 0.9.5 before 0.10.0, any authenticated user can overwrite the content of a message in a channel they do not belong to (including private and DM channels) by sending a chat completion request with a channel:-prefixed chat_id and a target message_id. The channel: path routes pipeline output through _make_channel_emitter, which writes to the Messages table using the caller-supplied message_id without binding it to the channel. This issue is fixed in version 0.10.0.

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vantage6: Algorithm developer can edit another developer's algorithm that is pending / under review

vantage6 is an open-source infrastructure for privacy preserving analysis. In version 5.0.2 and earlier, the algorithm-store edit permission lacks an ownership check, allowing one algorithm developer to alter another developer's algorithm while it is pending or under review. The attacker can change metadata including the algorithm image or image tag, causing reviewers and nodes to trust a different image from the one originally submitted for approval. No fixed version is available as of this review.

py-libp2p: yamux connection DoS via oversized data frame

py-libp2p is the Python implementation of the libp2p networking stack. In 0.7.0 and earlier, the yamux handle_incoming() method in libp2p/stream_muxer/yamux/yamux.py reads an attacker-controlled 32-bit DATA frame length with read_exactly() before validating it against MAX_WINDOW_SIZE or checking whether stream_id exists. A peer that completes the standard Noise handshake can send a 12-byte frame declaring a 0xFFFFFFFF body and then withhold the body, causing the sequential yamux read loop used by the default new_host() configuration to block and preventing every stream on that connection from making progress. No fixed version is available as

vLLM: Completion prompt lists fan out into unbounded engine requests

vLLM is an inference and serving engine for large language models. From 0.19.0 until 0.26.0, the /v1/completions CompletionRequest.prompt field in vllm/entrypoints/openai/completion/protocol.py accepts an unbounded list[str] or list[list[int]], prompt_to_seq() in vllm/renderers/inputs/preprocess.py and OnlineRenderer.preprocess_completion() in vllm/renderers/online_renderer.py expand every element, and vllm/entrypoints/openai/completion/serving.py creates one engine generator and response slot per prompt, allowing an authenticated API client to exhaust CPU, memory, async scheduling capacity, engine request slots, and response buffering with o

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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 activation_kernels.cu can cause act_and_mul_kernel to consume another batched user's input, allowing a request processed in the same inference batch to receive a partial or complete copy of another user's inference result. This issue is fixed in version 0.27.0.

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vLLM: Incomplete CVE-2025-62164 remediation can be bypassed by concurrent prompt parts

vLLM is an inference and serving engine for large language models. From 0.20.2rc0 until 0.26.0, safe_load_prompt_embeds in vllm/renderers/embed_utils.py uses torch.sparse.check_sparse_tensor_invariants, whose process-global save, enable, and restore state can be raced by concurrent prompt_embeds parts submitted to POST /v1/chat/completions through AsyncMultiModalItemTracker.resolve_items, asyncio.gather, and the default executor, allowing an invalid sparse tensor to reach tensor.to_dense despite the CVE-2025-62164 guard when enable_prompt_embeds is enabled. This issue is fixed in version 0.26.0.

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vLLM: ReDoS via structured_outputs.regex in the lm-format-enforcer backend (no compile timeout) — missed sibling of CVE-2026-55574

vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the structured_outputs.regex parameter in vllm/v1/structured_output/backend_lm_format_enforcer.py is passed to lmformatenforcer.RegexParser without compile_regex_with_timeout or validation in validate_structured_output_request_lm_format_enforcer, allowing an unauthenticated /v1/completions request against the lm-format-enforcer backend to consume a CPU core and stall the structured-output engine path with a catastrophic regular expression. This issue is fixed in version 0.26.0.

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vLLM: Unauthenticated Internal Path and Username Disclosure via Validation Error Messages

vLLM is an inference and serving engine for large language models. Prior to 0.26.0, the validation_exception_handler in vllm/entrypoints/openai/server_utils.py converts FastAPI RequestValidationError objects with str(exc), and sanitize_message in vllm/entrypoints/utils.py does not remove traceback-style file paths, allowing unauthenticated malformed JSON requests to /v1/chat/completions, /v1/completions, /tokenize, and /detokenize to disclose the OS username, home and virtual-environment paths, Python version, internal package structure, line numbers, and endpoint handler names. This issue is fixed in version 0.26.0.

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JupyterLab 4.6.0 Plugin Manager Lock-Rule Enforcement Bypass

JupyterLab (pip package 'jupyterlab') versions >=4.1.0,<=4.5.9 and >=4.6.0,<=4.6.1 contain a plugin manager lock-rule enforcement bypass. Two server-side enforcement gaps allow an authenticated user to circumvent administrator lock rules by making direct requests to the /lab/api/plugins endpoint, enabling or disabling plugins that were locked — including child plugins of multi-plugin extensions and plugins locked via the 'lock all' mechanism. This can impact data integrity and bypass hardening or restrictions (e.g., download/upload limits) implemented through locked plugins. Fixed in versions 4.6.2 and 4.5.10.

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JupyterLab before 4.6.2 Authentication Bypass via PyPIExtensionManager

JupyterLab versions >=4.6.0,<=4.6.1 and <=4.5.9 contain an allowlist/blocklist enforcement gap in PyPIExtensionManager.install(). A missing 'await' caused the is_install_allowed coroutine to never execute, so the extension allowlist/blocklist check was not enforced for direct callers of install(). The stock JupyterLab HTTP API and Extension Manager UI are not affected, as they perform a separate, correctly awaited check. The issue affects only deployments where a custom extension or downstream integration imports PyPIExtensionManager and calls install() directly with a package name influenced by untrusted input, an allowlist/blocklist is conf

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GitPython before 3.1.54 Remote Code Execution via kwarg value smuggling

GitPython versions before 3.1.54 contain a remote code execution vulnerability in the check_unsafe_options guard that can be bypassed by smuggling git options inside single-character kwarg values. Attackers can supply crafted option dictionaries to clone_from, fetch, pull, push, ls_remote, iter_commits, blame, or archive methods to execute arbitrary OS commands via the --upload-pack parameter.

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GitPython before 3.1.54 Arbitrary File Overwrite via diff

GitPython versions before 3.1.54 contain an arbitrary file overwrite vulnerability in the Diffable.diff method that fails to validate git options passed through kwargs. Attackers can supply the --output argument via the other parameter or output kwarg to write patch content to attacker-chosen file paths at process privilege level.

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GitPython before 3.1.54 Remote Code Execution via --template

GitPython before 3.1.54 contains an incomplete denylist in unsafe_git_clone_options that omits --template, allowing attackers to achieve arbitrary command execution during clone operations. Attackers can supply --template pointing to a directory containing malicious post-checkout hooks that execute when git clones the repository.

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