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    Veea Inc. Open-Sources Lobster Trap and Partners with NativelyAI to Advance Secure Agent Deployment

    3/2/26 5:47:56 PM ET
    $VEEA
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    BARCELONA, Spain, March 02, 2026 (GLOBE NEWSWIRE) -- At Mobile World Congress 2026 in Barcelona, Veea Inc. (NASDAQ:VEEA) today announced the open-source release of Lobster Trap, a lightweight security tool that monitors and enforces rules on interactions between AI agents and the language models that power them.

    Lobster Trap is available immediately under the MIT license at http://github.com/veeainc/lobstertrap and ships as a component of TerraFabric, Veea's control plane for governed autonomous systems at the edge.

    To accelerate enterprise adoption and embed conversation-layer security directly into development workflows, Veea is partnering with NativelyAI's builder community platform, lablab.ai, which has more than 250,000 AI developers building and deploying AI applications. Through this collaboration, Lobster Trap will be packaged within Native.Builder, NativelyAI's AI software production platform, enabling development teams to deploy AI agents with policy enforcement built in.

    The partnership places Lobster Trap inside an established AI builder ecosystem and accelerates the development of enterprise policy packs, reference integrations, and secure deployment templates for production environments.

    The Problem: AI Agents Are Getting Access. The Guardrails Haven't Kept Up.

    AI agents are increasingly given the ability to read files, write code, send messages, and take actions inside real business systems. That capability introduces significant risk to enterprises. A manipulated prompt or an unexpected model response can expose passwords, leak sensitive data, or trigger unintended actions.

    Most organizations today have no visibility into what their AI agents are asking a model to do, or what the model is responding with in return. Existing web and API security tools were not designed to inspect this conversational layer.

    "The industry has spent the last two years racing to give AI agents more power," said Allen Salmasi, Founder and CEO of Veea. "What has been missing is a practical way to observe and enforce policy at the point where the AI agents interact with AI models. Lobster Trap addresses that gap."

    How It Works

    Lobster Trap runs inline between AI agents and the language models they communicate with. Every prompt sent by the agent and every response returned by the model is evaluated against defined security policies before the agent is allowed to proceed.

    If a violation is detected, Lobster Trap can block the interaction, flag it for review, or log it for analysis.

    The scanning occurs under a millisecond and introduces no meaningful delay. The tool works with AI backends that use the standard OpenAI-compatible interface, allowing most deployments to adopt it without modifying application code.

    Security policies are defined in a configuration file. Out of the box, Lobster Trap detects prompt injection attempts, credential exposure, personal information leakage, suspicious file access, and data exfiltration patterns.

    NativelyAI Partnership: Enterprise Adoption and Secure Agent Templates

    Through its collaboration with NativelyAI and its community product LabLab.ai, Veea is supporting enterprise policy packs, reference integrations, and secure agent blueprints that make conversation-layer controls repeatable across teams and industries.

    By packaging Lobster Trap inside Native.Builder, development teams can launch agent-based applications with policy enforcement enabled by default. This reduces the need to retrofit controls later and simplifies secure deployment in enterprise environments.

    Why TerraFabric

    TerraFabric is Veea's control plane for governed autonomous systems at the edge. It manages distributed infrastructure as coordinated systems rather than isolated devices, applying policy enforcement, orchestration, and lifecycle control across fleets.

    Lobster Trap extends that governance model to the conversation layer. Where TerraFabric governs what workloads run and where they run, Lobster Trap governs what those workloads are allowed to ask and what they are allowed to receive from language models.

    In a TerraFabric deployment, Lobster Trap runs on local hardware. Prompt data, model responses, and audit logs can remain on-premises, supporting security and data sovereignty requirements.

    "Autonomy without control introduces risk," Salmasi said. "TerraFabric governs infrastructure. Lobster Trap governs AI conversation. Together, they give operators policy enforcement at every layer."

    Open source by design

    Veea is releasing Lobster Trap under the MIT license, enabling developers to use, modify, and extend it freely. The project is written in Go and compiles to a single file with no external dependencies, allowing it to run on Linux, macOS, or Windows.

    Availability

    Lobster Trap is available now at http://github.com/veeainc/lobstertrap.

    TerraFabric is currently in early deployments. Organizations interested in evaluating TerraFabric with integrated Lobster Trap capabilities can request early access at veea.com.

    About Veea Inc.

    Veea Inc. (NASDAQ:VEEA) is a global leader in AI-driven edge infrastructure. Founded in 2014 and headquartered in New York City, Veea's platform integrates connectivity, computing, cybersecurity, storage and AI in a unified solution for edge deployments ranging from SMBs to enterprise campuses, smart industries and remote communities. With more than 123 patents in related technology domains, Veea has been recognized by Gartner for its edge computing innovation. For more information, visit veea.com.

    About NativelyAI Inc.

    NativelyAI is an AI software production platform that mobilizes one of the world's largest developer ecosystems to build and deploy enterprise AI systems. Through structured build cycles and integrated production infrastructure, NativelyAI enables organizations to move from concept to secure, production-grade deployment at scale.

    Media Contact:

    Thomas Latiolais

    [email protected]

    Forward-Looking Statements

    Certain statements in this press release constitute "forward-looking statements." Such forward-looking statements are often identified by words such as "believe," "may," "will," "estimate," "continue," "anticipate," "intend," "expect," "should," "would," "plan," "predict," "forecasted," "projected," "potential," "seem," "future," "outlook," and similar expressions that predict or indicate future events or trends or otherwise indicate statements that are not of historical matters, but the absence of these words does not mean that a statement is not forward-looking. Forward-looking statements include, among other things, statements relating to the intended use of proceeds from our future offerings. These forward-looking statements and factors that may cause actual results to differ materially from current expectations include, but are not limited to: the ability of Veea to grow and manage growth profitably, maintain key relationships and retain its management and key employees; risks related to the uncertainty of the projected financial information with respect to Veea; risks related to the price of Veea's securities, including volatility resulting from changes in the competitive and highly regulated industries in which Veea plans to operate, variations in performance across competitors, changes in laws and regulations affecting Veea's business and changes in the combined capital structure; and risks related to the ability to implement business plans, forecasts, and other expectations and identify and realize additional opportunities. The foregoing list of factors is not exhaustive. All statements other than statements of historical facts included in this press release regarding the Company's strategies, prospects, financial condition, operations, costs, plans and objectives are forward-looking statements. Important factors that could cause the Company's actual results and financial condition to differ materially from those indicated in the forward-looking statements. Such forward-looking statements include, but are not limited to, risks and uncertainties including those regarding: the Company's business strategies, and the risk and uncertainties described in "Risk Factors," "Management's Discussion and Analysis of Financial Condition and Results of Operations," "Cautionary Note on Forward-Looking Statements" and the additional risk described in Veea's annual report on Form 10-K for the year ended December 31, 2024, quarterly reports on Form 10-Q, registration statements on Form S-1, and any other filings which Veea makes with the U.S. Securities and Exchange Commission. You should not rely upon forward-looking statements as predictions of future events. The forward-looking statements made in the press release relate only to events or information as of the date on which the statements are made in the press release. We undertake no obligation to update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, after the date on which the statements are made or to reflect the occurrence of unanticipated events except as required by law. You should read this press release with the understanding that our actual future results may be materially different from what we expect. Stockholders and prospective investors are cautioned not to place undue reliance on these forward-looking statements, which only speak as of the date made, are not a guarantee of future performance and are subject to a number of uncertainties, risks, assumptions and other factors, many of which are outside the control of Veea. Veea expressly disclaims any obligations or undertaking to release publicly any updates or revisions to any forward-looking statements contained herein to reflect any change in the expectations of Veea with respect thereto or any change in events, conditions or circumstances on which any statement is based. References for legal team (to be deleted prior to release)



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