Eight Critical Attack Vectors Exposed in AWS Bedrock: Understanding the Risks to Your Infrastructure
AWS Bedrock, Amazon’s platform for developing AI-driven applications, offers developers access to foundational models and tools that integrate these models with enterprise data systems. While this connectivity enhances functionality, it also presents significant security vulnerabilities. The XM Cyber threat research team has identified eight distinct attack vectors that exploit these connections, revealing how attackers could potentially manipulate and compromise Bedrock environments.
The Attack Landscape
The research indicates that each attack vector begins with low-level permissions that can escalate into severe breaches. The implications of these findings are critical for organizations leveraging AWS Bedrock, as they highlight the need for stringent security measures.
1. Model Invocation Log Attacks
AWS Bedrock maintains logs of all model interactions for compliance and auditing purposes. This logging mechanism can become a target for attackers. If an attacker gains access to the S3 bucket containing these logs, they can harvest sensitive data. In cases where direct access is restricted, attackers can redirect logs to a bucket they control using the bedrock:PutModelInvocationLoggingConfiguration permission. This enables them to capture every prompt without detection. Furthermore, attackers with s3:DeleteObject or logs:DeleteLogStream permissions can erase evidence of their activities, effectively eliminating any forensic trails.
2. Knowledge Base Attacks – Data Source
Bedrock Knowledge Bases link foundational models to proprietary enterprise data through Retrieval Augmented Generation (RAG). The data sources, such as S3 buckets and Salesforce instances, are directly accessible from Bedrock. An attacker with s3:GetObject access can bypass the model and extract raw data directly from the underlying sources. More critically, if they can retrieve and decrypt secrets, they can obtain credentials used by Bedrock to connect to integrated SaaS services, potentially allowing lateral movement into systems like Active Directory.
3. Knowledge Base Attacks – Data Store
The data store is where ingested information is indexed and structured for querying. Common vector databases integrated with Bedrock, such as Pinecone and Redis Enterprise Cloud, often have stored credentials that represent a weak link. An attacker with access to these credentials can retrieve sensitive endpoint values and API keys, gaining administrative access to vector indices. For AWS-native databases like Aurora and Redshift, compromised credentials can provide direct access to the entire structured knowledge base.
4. Agent Attacks – Direct
Bedrock Agents function as autonomous orchestrators. If an attacker gains permissions such as bedrock:UpdateAgent or bedrock:CreateAgent, they can modify an agent’s base prompt, leading to the leakage of internal instructions. This access allows for the insertion of malicious executors into legitimate agents, enabling unauthorized actions like database modifications or user creation under the guise of normal operations.
5. Agent Attacks – Indirect
Indirect attacks focus on the infrastructure supporting the agent rather than its configuration. An attacker with lambda:UpdateFunctionCode permissions can deploy malicious code directly to the Lambda functions utilized by agents. A variant of this attack involves using lambda:PublishLayer to inject harmful dependencies silently. Both methods can lead to the exfiltration of sensitive data or manipulation of model responses.
6. Flow Attacks
Bedrock Flows define the sequence of operations a model executes to complete tasks. An attacker with bedrock:UpdateFlow permissions can introduce unauthorized nodes into critical workflows, redirecting sensitive data to their controlled endpoints without disrupting application logic. This access can also modify condition nodes that enforce business rules, allowing unauthorized requests to bypass security checks. Additionally, attackers can swap the Customer Managed Key associated with a flow, ensuring that future data states are encrypted with their key.
7. Guardrail Attacks
Guardrails serve as Bedrock’s primary defense mechanism, filtering harmful content and blocking prompt injections. An attacker with bedrock:UpdateGuardrail permissions can systematically weaken these defenses, making the model more susceptible to manipulation. If an attacker possesses bedrock:DeleteGuardrail permissions, they can remove these safeguards entirely.
8. Managed Prompt Attacks
Bedrock’s Prompt Management system centralizes prompt templates across applications. An attacker with bedrock:UpdatePrompt permissions can directly alter these templates, injecting malicious instructions that could compromise the integrity of responses generated by the AI. Since prompt changes do not require application redeployment, attackers can modify AI behavior in real-time, complicating detection efforts.
Implications for Security Teams
These eight attack vectors underscore a common theme: attackers exploit the permissions, configurations, and integrations surrounding the model rather than targeting the model itself. A single over-privileged identity can redirect logs, hijack agents, poison prompts, or access critical on-premises systems from within Bedrock.
To secure AWS Bedrock, organizations must first understand their AI workloads and the permissions associated with them. This understanding should be complemented by mapping potential attack paths that traverse both cloud and on-premises environments, ensuring robust posture controls across all components of the stack.
For comprehensive technical details on each attack vector, including architectural diagrams and best practices, refer to the research available from XM Cyber. According to publicly available thehackernews.com reporting, this resource provides valuable insights for organizations looking to fortify their defenses against these emerging threats.
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