AWS Bedrock: The IA that could open the door to chained attacks all over your company

Amazon Bedrock has opened a huge door for companies to integrate advanced language models into their processes: it allows models not only to answer questions, but also to consult CRMs, activate serverless functions and recover corporate repository information. That same bridge between IA and business systems is what multiplies the risk, because it transforms the IA agent into another element of the infrastructure with own permissions, network reach and attack vectors.

A security research team from XM Cyber has decoupled this problem and validated several attack routes that take advantage of this connectivity. It is not a question of breaking the box of a model by gross force, but of abusing the credentials, configurations and permissions surrounding Bedrock to reach valuable resources outside the inference engine itself. For those who manage or secure an environment in AWS, it is a clear lesson: protecting models is not enough if the context around them is not assured. You can check the Bedrock page on AWS to better understand their capabilities: https: / / aws.amazon.com / bedrock /, and the complete study of XM Cyber with diagrams and technical recommendations is available at: Building and Scaling Secure Agenic AI Applications in AWS Bedrock.

An attack path that draws attention is the one that uses the model's invocation records. Bedrock keeps traces of each interaction for audit reasons; these files may contain sensitive prompts or PII results. If a malicious actor can read the bucket where those logs are stored or redirect them to a destination under their control, gets a continuous flow of information. In another related scenario, anyone who has permissions to remove objects in S3 or delete streams from logs can delete tampering prints or jailbreak, complicating forensic investigation and maintaining undercover access.

The Knowledge Bases architecture in Bedrock - the pattern known as Retrieval Augmented Generation (RAG) - is another critical surface. Here the original data sources (S3, SharePoint, Salesforce, Confluence) coexist with the indexes and stores that make that content consultable. If an attacker achieves credentials to read the source directly, he can exfilter data without going through the model; if he steals secrets that Bedrock uses to connect with SaaS services, he can move laterally to identity systems like Active Directory. The difference between reading raw data and manipulating the connection is the difference between espionage and a door for climbing privileges.

By supplementing this, the place where information already processed is stored - vector bases or structured warehouses - has its own risk. Commercial vector platforms or managed services may contain keys and endpoints in configurations that, if read through Bedrock APIs (for example through requests that recover configuration objects), allow the attacker to control integers or clone data. With AWS native bases such as Aurora or Redshift, the theft of credentials can give direct access to tables and complete relational information.

Bedrock agents are self-contained elements designed to orchestrate tasks. Access to create or update agents can change your base instruction and the tools you use, causing unwanted uses: from the disclosure of internal instructions to the annexation of malicious executors acting as "back doors" and making changes to databases or user accounts in the name of legitimate flow. In this type of scenario, malicious action is camouflaged within the expected behavior of the agent, which makes detection difficult.

There is also a more subtle vector: instead of touching the agent, the attacker compromises the infrastructure that the agent invokes. If an actor can update the code of a Lambda function or publish layers with malicious dependencies, inject harmful behavior into the calls the agent makes to external tools. It is an efficient way to pollute an execution chain without directly modifying the agent.

Flows that define steps and conditions to complete tasks can also be manipulated. By changing a flow you can insert a node that sends sensitive data to an external storage, alter conditions that act as business controls to allow unauthorized requests, or even replace the encryption key used for states and snapshots with one controlled by the attacker. Thus, business logic continues to work apparently well while the confidentiality or integrity of information is compromised.

Bedrock's "guards" are the first line of defense to prevent the model from generating dangerous content, accepting prompt injections or exposing personal data. If someone can lower thresholds, remove rules or remove those filters, much of the control that organizations think they have disappears. The handling of guards transforms logical vulnerabilities into operational gaps because it reduces resilience to malicious inputs that were previously blocked.

Finally, the centralized management of prompt templates offers a large-scale impact vector. Modifying a shared temperate (or its active version) can be inserted instructions that change the model's behavior in all the applications that consume it, without the need for a withdrawal. This type of "hot" change allows mass exfiltration or coordinated generation of harmful content and is difficult to detect with traditional application monitoring.

What should security teams do? The good news is that the defenses are still always applied with discipline: governance of identities and access as strict as possible (principle of lesser privilege), control and monitoring of login with guaranteed integrity, secure management of secrets and keys, network segmentation and limitation of the scope of agents and functions. It is essential to maintain an inventory of IA loads and map what resources they have access to, because, as the analysis shows, a single excessive permit can be sufficient to trigger a chain attack. AWS offers good practice guides for IAM that are a good starting point: https: / / docs.aws.amazon.com / IAM / latest / UserGuide / best-practices.html.

Observability is equally critical: to ensure that CloudTrail and registration systems cannot be easily redirected or deleted, and to encode sensitive logs with keys whose access is restricted and audited. The CloudTrail and AWS key management guides help design these protections: CloudTrail and AWS KMS. For the specific risk of RAG and vector bases it is appropriate to review controls around ingestion pipelines, rotation of credentials, and network restrictions that limit the possibility of extracting complete indices from outside the VPC or the environment administered.

At the IA governance level it is useful to look at risk management frameworks and frameworks that help prioritize technical and organizational controls. The NIST has published guidance on risk management for IA which can serve as a reference for policies and processes: NIST TO RMF. Implement change reviews for guards, prompt templates and flows, and subject modifications to approval processes with traceability reduces the possibility of undetected handling.

In short, Bedrock and similar platforms force us to think about the security of the IA in broad terms: to protect models, yes, but above all to protect the accounts, integration routes and infrastructure parts that allow an agent to touch the rest of the organization. If you want to go into concept tests, diagrams and operational recommendations, XM Cyber's full report contains that technical material and was contributed by team researchers, including Eli Shparaga: Building and Scaling Secure Agenic AI Applications in AWS Bedrock.