More than 9,300 Amazon Web Services (AWS) access keys publicly exposed between August 2022 and August 2026 are still active and valid.

Truffle Security has been tracking this exposure for the past four years and says that 817 of the exposed keys were linked to companies, 526 of them being AWS root keys.

According to the researchers, 242 of the keys are associated with Identity and Access Management (IAM) users with the AdministratorAccess policy. This role has full permissions to create, modify, delete, and view virtually all AWS services and resources within an account.

They note that each key of the 768 live keys in the two sets “full control of a company’s AWS account.”

The company found 431,875 AWS secrets across code repositories, Git history, datasets, Docker images, registries, and CI logs and extracted 64,024 unique AWS keys that corresponded to 50,654 AWS accounts after removing duplicates.

However, the subset for which the researchers had complete credentials that could be used for re-verification was 10,616 keys, and 88% of them continued to authenticate as of August 10.

Amazon Web Services (AWS) is Amazon’s cloud-computing platform used by companies to host websites and applications, store data, run databases and servers, manage domains, and operate their online infrastructure.

Full control of a company’s AWS account could allow an attacker to access, exfiltrate, or wipe cloud-hosted data, take control of servers and applications, and create rogue admin accounts for persistent access

Threat actors could also use their access to deploy cryptominers, generating substantial charges for the company. Truffle Security says that only 262 of 2,754 readable accounts had a budget alert set up.

Hugging Face, a popular online platform where developers share AI models, datasets, and applications, was the largest single source of leaked AWS keys, accounting for 8,482 unique key exposures.

Also, 17.9% of those keys were root, meaning the highest-privileged identity, which isn’t restricted by IAM permissions.

Truffle Security found that, for the 2,903 keys with available creation dates, the median age was 1,831 days (about five years), while the oldest had existed for 17.4 years.

Only 398 (13.7%) of those entries had a newer access key associated with the same user, suggesting most had never been rotated.

To defend against potential abuse, the researchers recommend deleting all root access keys, reviewing IAM credentials by age, rotating or revoking exposed keys, and configuring budget alerts.

Also, any credential committed to a public source should be treated as compromised.

Truffle Security said its testing was limited to read-only metadata, and that it has notified all identifiable owners of the exposed credentials.

Overall prevention scores can hide what happens after initial access. Once attackers are using valid credentials, prevention drops sharply.

The Blue Report 2026 measures defenses technique by technique across 338 million simulations run in customer production environments.

Get the report

When an agentic AI system hands a task from a small model to a larger one — or back down again — it pays a steep tax: the receiving model has to recompute the entire conversation from scratch, driving up compute costs and latency. This is a major bottleneck for enterprises building long-horizon, multi-LLM workflows.

To solve this challenge, researchers at Nvidia have introduced a cross-model KV cache transfer technique that directly maps the prefilled KV cache from a source model into the target model. This technique aligns with real-world agentic applications where large contexts accumulate across many turns. 

For real-world AI applications, cross-model KV cache transfer can reduce compute costs and latency on long-running, multi-LLM workflows — and it does so with simple linear math, not an expensive deep learning model.

Experiments show that, on compatible model pairs, this linear mapping process runs 2.7 to 25 times faster than recomputing the conversation while retaining up to 98% of the target model’s standalone accuracy. 

Why swapping models mid-session is so expensive

Examining how LLMs handle memory helps understand why multi-model workflows hit a performance wall in production. When an LLM receives a prompt, it must first execute the “prefill” stage, which is the initial forward pass that computes the keys and values for all input tokens and populates the Key-Value (KV) cache. 

After that, it enters the “decode” phase, where it computes and generates the next tokens in the sequence. During this phase, the model reads from this KV cache to predict new tokens one by one, bypassing the need to re-evaluate the entire history of the conversation for each new token.

In multi-turn conversations or long-horizon agentic sessions, the context gradually becomes longer. Because the computational cost of the prefill stage scales directly with both model size and input length, processing these long sessions becomes increasingly expensive and introduces significant latency if the KV cache is invalidated.

This invalidation happens whenever the AI system tries to swap models mid-session, such as routing a complex reasoning step to a larger model or dropping to a smaller model to save costs. Because different LLMs have different architectures, they expect their cache inputs in different formats. 

As a result, any model switch forces the receiving model to repay the entire prefill cost from scratch to recompute the KV cache for the accumulated context. 

Mapping memory between models without starting over

The Nvidia researchers studied cross-model KV cache transfer to see how developers can transform the KV cache of one model into the expected format of another without running the prefill phase again. 

If solved, cross-model KV cache transfer has benefits in both directions. Small-to-large model transfer upgrades the quality of the output. For example, a cheap, small model handles the routine parts of an agentic workflow but struggles with a complex reasoning problem, and you map the KV cache to a larger model and continue the process seamlessly.

On the other hand, large-to-small model transfer reduces compute costs. A highly capable, large model might be used to unpack a massive, complex system prompt or synthesize a dense PDF at the start of a session. Once the heavy lifting is done, the session’s KV cache is mapped down to a smaller, more economical model to handle the rapid-fire, conversational turns that follow.

There have been previous efforts to solve the KV cache transfer problem, but they suffer from a few key limitations. These include the need for expensive gradient-based training or very strict architectural constraints.

For this initial study, the authors restricted their focus to within-family transfers, such as transitioning between different-sized models in the Qwen, Llama, or Ministral families. These models share tokenizers, training data DNA, and core architectural styles but differ in size and depth. However, this framework leaves plenty of room for future experiments. The researchers note the technique could eventually be expanded to cross-family transfers, mismatched KV head counts, or hybrid architectures that blend standard attention with other memory mechanisms.

The key finding of the Nvidia study is that cross-model KV cache is a significantly linear structure. This means you can do the mapping with simple algebra tricks and without the need for heavy neural network training. For example, when experimenting on KV cache transfer from a 14-billion parameter Qwen3 model to a 32-billion parameter version, the authors discovered that a simple linear regression mapping from one source layer to a target layer can recover 56% of the variance in the target’s keys and 32% of the variance in its values. When combining multiple source layers, those numbers climbed to 79% and 65% respectively.

To translate this linear relationship into a practical system, the researchers designed a closed-form per-head ridge mapper with three key components:

• Per-head ridge regression: Instead of using complex deep learning to train the system, they fit a simple linear regression using a tiny calibration set of a few hundred text sequences. This technique solves a classic line-of-best-fit problem independently for every attention head.

• Cross-layer source selection: Because the source and target models have different numbers of layers, the mapper evaluates and selects the most predictive source layers to feed into each specific target layer. This way, the system picks only the most helpful pieces of memory from the old model to construct the new model’s memory.

• Content-space mapping: Before translating the data, the mapper strips away the RoPE encodings. RoPE, or Rotary Position Embedding, is a standard mechanism that applies a mathematical, position-dependent rotation to the data so the model understands the order of the tokens in a sequence. Stripping the RoPE values makes it possible for the mapper to generalize to sequences of lengths larger than its training data.

Putting the linear mapper to the test

To test whether the technique works, the researchers evaluated the transfer pipeline across six “matched-KV” model families. Matched-KV means the source and target models share the same KV head count and per-head dimensions, which is typical for different-sized models within the same family.

The model families included Qwen3, Llama 3.1, and Ministral 3, with tests for KV cache transfer across different sizes ranging from 3 billion to 70 billion parameters. Their experiments included a massive 8.8x parameter leap from Llama 3.1 8B to 70B.

To cover a wide range of tasks, they evaluated the models on five core accuracy benchmarks (ARC-Challenge, HellaSwag, WinoGrande, MMLU, and GSM8K) as well as language modeling perplexity on WikiText-2 and a multi-turn conversation task called CoQA. To fit the linear translation mapper, they used a tiny calibration dataset of just 500 text sequences of 1,024 tokens each.

The researchers compared the framework against the baseline ceiling accuracy where the target model does a full, traditional prefill. They also compared their full system against ablated configurations, such as reducing the number of selected layers or deactivating different components. Additionally, they compared their simple method against a deep neural network trained with backpropagation to see if heavier deep learning could recover accuracy on pairs where the linear method struggled.

For four of the six tested pairs, the fast, closed-form linear ridge mapper retained 73% to 98% of the target’s standalone prefill accuracy — including the massive leap from Llama 3.1 8B to 70B, which retained 72.8% of target accuracy.

The mapper also runs between 2.7 and 25 times faster than re-prefilling. For example, when translating a 32,768-token KV cache from a Qwen3 14B to a 32B model, the transfer took just 278 milliseconds, compared to nearly 7 seconds for a standard re-prefill.

The system also demonstrated high stability on tasks that run across many steps. When tested on multi-turn conversations, the drift, or accuracy loss, between the target baseline and the transferred cache remained incredibly small across 10 turns, proving it will not cascade into failure during long agentic sessions.

However, the straightforward linear approach did run into limitations on specific model pairs. For two of the Ministral configurations, the linear mapper degraded sharply because the simple linear fit failed to extrapolate outside calibration data. To fix this, the researchers swapped the linear mapper for a nonlinear multi-layer perceptron (MLP) with two 1,024-unit hidden layers trained on the same data. This added a complexity and training tax to the setup, but it recovered their accuracy to above 90%.

A bigger industry problem than one paper can solve

The introduction of cross-model transfer is part of a broader, industry-wide push to solve the KV cache bottleneck, which has emerged as one of the key hurdles for scaling enterprise AI. As developers push LLMs to process massive documents or code bases and execute long-running reasoning tasks, managing this memory layer is becoming as important as the models themselves.

Over the past year, researchers have attacked this compute and memory problem from multiple angles. For instance, Nvidia recently introduced dynamic memory sparsification (DMS), a technique that intelligently evicts less important tokens from the KV cache to cut reasoning costs by up to 8x. 

Other approaches focus on aggressive data compression. MIT researchers developed an algebraic compaction technique called Attention Matching that compresses the KV cache by 50x without degrading quality. Similarly, Nvidia introduced KV Cache Transform Coding (KVTC), which borrows media compression concepts to shrink memory by 20x without altering the underlying model weights.

Beyond compression, researchers are also attacking the computational overhead of memory retrieval. Optimizers like IndexCache strip away redundant layer calculations to deliver significantly faster time-to-first-token in long-context applications. And models like DeepSeek and the GLM series are optimizing the KV cache through architecture innovations.

As AI systems take on longer-horizon tasks and more complex architectures, the underlying memory infrastructure is becoming as important as the models themselves. Cross-model KV cache transfer gives developers one more tool for keeping inference costs down as they scale multi-model agentic systems.

£44.99 is Prime Day pricing, and Ring’s newest doorbell is down to that price right now.

The Ring Battery Doorbell (2nd Gen) has dropped to £44.99, down 44% from its £79.99 RRP, a £35 saving that puts it firmly inside Prime Day territory months before that event actually rolls around again.