AI & LLM Vulnerabilities

A vulnerability (CVE-2026-5002) was discovered in PromtEngineer localGPT that allows injection attacks (inserting malicious code into input) through the LLM Prompt Handler component in the backend/server.py file. An attacker can exploit this vulnerability remotely, and the exploit code has been publicly released. The vendor has not responded to disclosure attempts, and because the product uses rolling releases (continuous updates without traditional version numbers), specific patch information is unavailable.

Giskard Agents contain a server-side template injection vulnerability in the `ChatWorkflow.chat()` method, which treats user input as Jinja2 template code (a templating language that processes special syntax) instead of plain text. If a developer passes user-provided data directly to this method, an attacker can execute arbitrary code on the server by embedding malicious Jinja2 syntax in their input.

Nanobot, a personal AI assistant, had a vulnerability in its email module that allowed attackers to send malicious prompts via email, which the bot would automatically process as trusted commands without the owner's knowledge. This is a type of indirect prompt injection (tricking an AI by hiding instructions in its input) that could let attackers run arbitrary system tools through the bot. Version 0.1.6 fixes this flaw.

LibreChat (a ChatGPT alternative with extra features) versions 0.8.2-rc2 through 0.8.2 have a vulnerability that allows attackers to access internal systems through SSRF (server-side request forgery, where an attacker tricks a server into making requests to resources it shouldn't access). Even though a previous SSRF fix was applied, it only checked domain names and didn't verify whether those names actually point to private IP addresses (internal network addresses), leaving the system exposed.

LibreChat, a ChatGPT alternative with extra features, has a security flaw in versions before 0.8.3 where a function called `isPrivateIP()` fails to recognize IPv4-mapped IPv6 addresses (IPv6 addresses that contain IPv4 address information) in a certain format, allowing logged-in users to bypass SSRF protection (SSRF is server-side request forgery, where an attacker tricks a server into making requests to internal networks it shouldn't access). This could let attackers access sensitive internal resources like cloud metadata services and private networks.

LangChain Core has a path traversal vulnerability (a security flaw where attackers can access files outside intended directories using '../' sequences or absolute paths) in legacy functions that load prompt configurations from files. When an application accepts user-influenced prompt configs and passes them to `load_prompt()` or `load_prompt_from_config()`, attackers can read arbitrary files like secret credentials or configuration files, though they're limited to specific file types (.txt, .json, .yaml).

Langflow had a vulnerability where the code checking if a user owned a flow was missing when authentication was enabled, allowing any authenticated user to read, modify, or delete flows belonging to other users, including stealing embedded API keys. The fix removes the conditional logic and always checks that the requesting user owns the flow before allowing any operation.

The @mobilenext/mobile-mcp package has a path traversal vulnerability (a security flaw where an attacker can write files outside the intended directory by using special path characters like `../`) in its `mobile_save_screenshot` and `mobile_start_screen_recording` tools. The `saveTo` and `output` parameters are passed directly to file-writing functions without checking if the paths are valid, allowing an attacker to write files anywhere on the system.

The Azure Data Explorer MCP Server (adx-mcp-server) has KQL injection vulnerabilities (a type of code injection where untrusted input is inserted into database queries) in three tools that inspect database tables. Because the `table_name` parameter is directly inserted into Kusto queries (Azure's query language) using f-strings without checking or cleaning the input, an attacker or a prompt-injected AI agent can execute arbitrary database commands, including reading sensitive data or deleting tables.

In MLflow (a machine learning tool for managing experiments), when basic authentication is enabled, certain endpoints that show trace information (a record of how the AI made decisions) and allow users to assess traces are not properly checking user permissions. This means any logged-in user can view traces and create assessments even if they shouldn't have access to them, risking exposure of sensitive information and unauthorized changes.