Formula Injection in Actix with Dynamodb

Formula injection occurs when user-controlled input is interpreted as a formula or expression by a downstream system. In an Actix web service that uses Amazon DynamoDB as its persistence layer, this typically arises when query parameters or JSON payload values are placed into DynamoDB expression attribute values without proper sanitization or type validation. Although DynamoDB does not natively evaluate formulas, the risk emerges at the application layer: Actix handlers construct requests (e.g., UpdateItem or ConditionCheck) using string interpolation or concatenation to build ExpressionAttributeValues or filter expressions, and those values may contain inputs like =1+2, ="malicious", or structured strings that later downstream tooling or export processes interpret as executable content.

In this specific combination, the vulnerability is not in DynamoDB itself but in how Actix builds and sends requests. For example, an endpoint that accepts a numeric parameter for an UpdateItem operation might directly embed user input into an update expression such as SET #val = :val. If the input is = 1+2 and the handler builds the expression by string concatenation, the resulting expression may be misinterpreted by downstream consumers (e.g., logging parsers, export jobs, or admin tooling) that parse the stored value as a formula. Additionally, if Actix exposes endpoints that return raw attribute values to frontends that evaluate formulas (e.g., in spreadsheets or low-code tools), stored values crafted via injection can trigger unintended recalculations or data corruption when rendered.

The 12 security checks in middleBrick surface this class of issue by testing input validation and property authorization. An unauthenticated scan can send values designed to appear as formulas or structured injection payloads, then inspect responses and side effects to detect unsafe construction patterns. Findings highlight missing type constraints, missing allowlists for numeric input, and unsafe usage of expression-building helpers. Remediation guidance emphasizes strict input validation, type-safe expression construction, and avoiding concatenation when building DynamoDB expressions in Actix handlers.

Secure DynamoDB interactions in Actix rely on using the official AWS SDK for Rust with the builder pattern, strongly typed structures, and expression attribute maps instead of string concatenation. Below are concrete, idiomatic examples that prevent formula injection by validating inputs and safely constructing requests.

1. Safe UpdateItem with ExpressionAttributeValues

Always use placeholders for both attribute names and values. Validate and parse user input into the expected type before constructing the request.

use aws_sdk_dynamodb::types::AttributeValue;
use aws_sdk_dynamodb::Client;
use std::collections::HashMap;

async fn update_item_safe(client: &Client, table: &str, key: &str, user_value: &str) -> Result<(), aws_sdk_dynamodb::Error> {
    // Validate and parse input according to your domain rules.
    // For numeric fields, parse explicitly; reject malformed input.
    let parsed_value = user_value.parse::().map_err(|_| {
        aws_sdk_dynamodb::error::PutItemError::validation_error("Invalid number")
    })?;

    let mut expression_attribute_names = HashMap::new();
    expression_attribute_names.insert("#val", "value");

    let mut expression_attribute_values = HashMap::new();
    expression_attribute_values.insert(":val", AttributeValue::N(parsed_value.to_string()));

    let resp = client.update_item()
        .table_name(table)
        .key("id", AttributeValue::S("my-id".to_string()))
        .update_expression("SET #val = :val")
        .expression_attribute_names(&expression_attribute_names)
        .expression_attribute_values(&expression_attribute_values)
        .send()
        .await?;

    Ok(())
}

2. ConditionCheck with type-safe filter expressions

Avoid embedding raw input in condition expressions. Use expression attribute values and validate inputs before constructing the condition.

async fn condition_check_safe(client: &Client, table: &str, expected_number: i64) -> Result<(), aws_sdk_dynamodb::Error> {
    let mut expression_attribute_values = HashMap::new();
    expression_attribute_values.insert(":threshold", AttributeValue::N(expected_number.to_string()));

    let resp = client.condition_check_item()
        .table_name(table)
        .key("id", AttributeValue::S("item-123".to_string()))
        .condition_expression("attribute_exists(#id) AND #num = :threshold")
        .expression_attribute_names(&{ 
            let mut m = HashMap::new(); 
            m.insert("#id", "id"); 
            m.insert("#num", "number"); 
            m 
        })
        .expression_attribute_values(&expression_attribute_values)
        .send()
        .await?;

    Ok(())
}

3. Batch operations with validated items

When using BatchWriteItem, ensure each request’s attribute values are constructed from validated, typed sources rather than raw strings that may contain formula-like content.

async fn batch_write_safe(client: &Client, table: &str, items: Vec<(String, i64)>) -> Result<(), aws_sdk_dynamodb::Error> {
    let mut write_requests = Vec::new();

    for (id, numeric_val) in items {
        // Validate numeric_val according to business rules before use.
        let mut request = aws_sdk_dynamodb::types::WriteRequest::default();
        let mut put_request = aws_sdk_dynamodb::types::Put::default();
        put_request.item(
            "id",
            AttributeValue::S(id)
        );
        put_request.item(
            "value",
            AttributeValue::N(numeric_val.to_string())
        );
        request.put_request(put_request);
        write_requests.push(request);
    }

    let resp = client.batch_write_item()
        .request_items(table, write_requests)
        .send()
        .await?;

    Ok(())
}

These patterns emphasize explicit parsing, expression attribute maps, and strong typing, which prevent attacker-supplied values from being misinterpreted as executable expressions or instructions by downstream tooling. Combine these practices with input allowlists and schema validation in your Actix routes to reduce the attack surface presented by formula injection paths.