Tech
ElizaOS Exposed: Researchers Discover How to Manipulate Its Memory and Alter Its Operations
TL;DR
- Princeton researchers discovered how to manipulate the memory of AI agents like ElizaOS to alter their financial decisions.
- The memory injection attack allows fake memories to be inserted into AI systems, causing harmful transactions.
- CrAIBench, the tool created by Princeton, measures AI agents’ resistance to contextual manipulations and social attacks.
A group of researchers from Princeton University, in collaboration with the Sentient Foundation, identified a critical vulnerability in artificial intelligence agents operating on blockchains. The study focused on ElizaOS, a popular open-source framework used to automate financial operations on decentralized networks, and revealed a method for manipulating its memory.
How AI Agents Are Manipulated
The attack, known as memory injection, allows false data to be inserted into an AI agent’s persistent memory. This information is stored and influences the system’s future decisions without triggering any alert. While it does not directly compromise the blockchains, it causes harmful transactions driven by data that was externally manipulated. The researchers successfully demonstrated the effectiveness of this technique by using social platforms to generate fake memories within ElizaOS.
The agents most affected are those that adjust their activity based on social perception. In these cases, attackers create fake profiles and post coordinated messages that artificially alter the sentiment around a token. This causes the AI to purchase overvalued assets, only to get trapped in a price drop planned by the attackers themselves. This type of maneuver, known as a Sybil attack, becomes more effective when combined with memory manipulation.

ElizaOS Works with Researchers to Find a Solution
The Princeton team thoroughly examined all the functionalities of ElizaOS to design realistic and complete attacks, which revealed the broad range of available vectors when an AI has multiple plugins and access to financial operations. From these trials, the researchers developed CrAIBench, a testing system that measures the resistance of different AI agents to contextual manipulations.
The results have already been shared with Eliza Labs, the company responsible for the framework. Discussions about possible solutions are ongoing. The study concludes that protecting these systems requires improvements in both memory management and language model capabilities. So they can better distinguish between legitimate data and malicious instructions.
The post ElizaOS Exposed: Researchers Discover How to Manipulate Its Memory and Alter Its Operations appeared first on The Cryptocurrency Post.
Tech
Bittensor introduces Gamma Tokens as payment layer
Bittensor has introduced Gamma Tokens as a native payment system for its AI economy, giving users and AI agents a way to pay for subnet services with a Bittensor wallet rather than separate accounts or API keys for each subnet.
The OpenTensor Foundation said Gamma is designed as a prepaid, dollar-valued service credit system for subnet services. In practice, the update is meant to simplify how customers access services across the network while keeping the payment process inside the Bittensor wallet flow.
According to the release, Gamma lets subnets charge for services in dollar-valued credits. Customers fund a deposit by burning Subnet Alpha, then authorize a services spender to charge that deposit as usage accrues. The system also uses a hotkey as an API key, creating a standardized payment layer across subnets.
The foundation described the mechanism as a way to budget usage in dollars while service demand translates into Alpha burn. That structure makes Gamma less about a separate speculative token and more about how subnet services are paid for inside Bittensor’s network design.
For readers following AI-focused crypto projects, the key change is operational rather than promotional: Bittensor is formalizing a payment standard for subnet services, with unified wallet access replacing a more fragmented setup. The announcement did not add further detail on adoption or usage levels at the time of publication.
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Tech
io.net highlights idle GPU supply as AI compute demand grows
io.net is again framing its network around a familiar bottleneck in AI infrastructure: builders need compute, while some GPU capacity remains unused. In an official post on X, the company said startups are burning runway while waiting for compute and argued that idle GPUs can be connected to builders at prices they can afford.
Startups are burning runway waiting for compute.
Meanwhile, GPUs sit underused.
There’s a solution.
Connect that capacity to the builders who need it, at prices they can afford.
At @ionet, we want your next funding round to pay for progress. Not for coffee while you wait on a… pic.twitter.com/Ljzh9LPhTJ
— io.net (@ionet) November 8, 2026
The post positions io.net as a coordination layer for matching spare GPU capacity with AI workloads, with the company saying the goal is to help founders spend funding on product progress rather than waiting on hardware. The message aligns with io.net’s broader pitch in its official blog post on AI infrastructure, which describes the project as an orchestration layer for aggregating and coordinating underutilized GPU capacity.
The update does not introduce a new product launch or a fresh technical rollout, but it does restate the core use case io.net is presenting to the market. That use case centers on making idle compute available to AI builders without requiring them to wait for new capacity to come online.
io.net’s framing also suggests the company is continuing to emphasize affordability and access as the main draw for its network. Rather than presenting itself as a new source of hardware, it is pitching a way to make existing GPU supply more usable for developers who need it now.
For readers following AI-focused crypto projects, the post is best read as a reaffirmation of io.net’s positioning rather than as evidence of a new operational milestone. The company is making a case for coordination over expansion: use the GPUs that already exist, and route them toward builders who can put them to work.
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Tech
NEAR launches Agent Market job board for local services quote agent
NEAR has added a new job posting to its Agent Market for a Local Services Quote Agent, an AI agent designed to contact local providers, collect responses and return side-by-side comparisons of price and availability.
The listing appeared on the official NEAR DevHub channel and was also amplified by the project’s official social accounts. According to the post, the builder who takes on the job would keep ownership of the agent and retain the revenue it generates.
The job description frames the agent as a tool for gathering real quotes from local services rather than a general chatbot. It would reach out to providers, log the replies and organize the results so users can compare options more easily.
Builder-owned agents remain central to the marketplace pitch
NEAR has positioned the Agent Market around the idea that developers can publish agents, set their terms and keep ownership after the job closes. In the official posts, the company said builders keep the agent and can earn when it is hired.
The job board format also suggests a more structured approach to AI agent development, with specific tasks posted for builders to bid on. In this case, the task centers on a practical consumer workflow: collecting quotes from local providers and presenting them in a usable format.
NEAR did not say when the Local Services Quote Agent will be completed or who will take the job. The posting does, however, show that the Agent Market is actively listing new work and pushing the marketplace model as part of its product rollout.
The post NEAR launches Agent Market job board for local services quote agent appeared first on The Cryptocurrency Post.
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