Buffer Overflow in Spring Boot with Bearer Tokens

A buffer overflow occurs when data exceeds the allocated memory boundary, potentially overwriting adjacent memory and leading to arbitrary code execution or crashes. In Spring Boot applications that rely on Bearer Tokens for authentication, the risk typically emerges not from Spring itself, which manages memory safely, but from downstream components that process token values unsafely. For example, if a Bearer Token is passed into native code via Java Native Interface (JNI), or forwarded to a vulnerable library that performs unchecked copying, an oversized token can trigger a classic buffer overflow in that native layer.

More commonly in API security testing, the concern is not a traditional memory corruption overflow but logical boundary violations. A Spring Boot endpoint that accepts a Bearer Token via the Authorization header may pass the raw token string to parsers or regex checks that operate on fixed-size buffers. If an attacker supplies a token longer than expected, the application might exhibit denial-of-service behavior or expose sensitive data through error messages or logs. In black-box scans, middleBrick tests such behaviors by submitting unusually long Bearer Tokens and observing responses, looking for stack traces, crashes, or data leakage that indicate weak input validation.

When OpenAPI specs are available, middleBrick correlates the authorization mechanism defined in the spec with runtime behavior. If the spec declares Bearer authentication but does not enforce token length constraints, and the implementation reflects that gap, an attacker can probe the unauthenticated attack surface with oversized tokens. This aligns with BFLA/Privilege Escalation checks, where excessive or missing authorization on token acceptance can allow unauthorized actions. The scanner’s input validation checks search for missing bounds on header values, ensuring that tokens are validated for format and length before use, reducing the chance of buffer-related flaws.

Additionally, SSRF and Data Exposure checks consider how Bearer Tokens are handled when the application forwards requests. If a token is embedded in URLs or logs without sanitization, it can leak into external systems or error payloads. middleByte’s active probe sequence includes sending tokens with embedded newline or control characters to detect improper handling. By combining spec analysis with runtime tests, the scanner identifies whether token processing introduces buffer-related weaknesses, even when the overflow is logical rather than strictly memory-based.

Remediation focuses on strict validation of Bearer Tokens, enforcing length limits, and avoiding unsafe propagation. In Spring Boot, configure security to reject malformed or excessively long tokens before they reach business logic. Use a filter or interceptor to validate the Authorization header format and size, returning 400 for invalid inputs.

@Component
public class BearerTokenValidationFilter extends OncePerRequestFilter {

    private static final int MAX_TOKEN_LENGTH = 4096;
    private static final Pattern BEARER_PATTERN = Pattern.compile("^Bearer [A-Za-z0-9\\-._~+/=]+$");

    @Override
    protected void doFilterInternal(HttpServletRequest request, HttpServletResponse response, FilterChain filterChain)
            throws ServletException, IOException {
        String authHeader = request.getHeader("Authorization");
        if (authHeader != null && !authHeader.isBlank()) {
            if (authHeader.length() > MAX_TOKEN_LENGTH) {
                response.sendError(HttpServletResponse.SC_BAD_REQUEST, "Authorization token too long");
                return;
            }
            if (!BEARER_PATTERN.matcher(authHeader).matches()) {
                response.sendError(HttpServletResponse.SC_BAD_REQUEST, "Malformed Authorization header");
                return;
            }
        }
        filterChain.doFilter(request, response);
    }
}

Register this filter early in the security chain to ensure invalid tokens are rejected before authentication mechanisms process them. In your security configuration, integrate the filter before any authentication filters:

@Configuration
@EnableWebSecurity
public class SecurityConfig {

    @Bean
    public SecurityFilterChain filterChain(HttpSecurity http, BearerTokenValidationFilter validationFilter) throws Exception {
        http.addFilterBefore(validationFilter, UsernamePasswordAuthenticationFilter.class);
        http.authorizeHttpRequests(auth -> auth
            .requestMatchers("/public/**").permitAll()
            .anyRequest().authenticated()
        );
        http.oauth2ResourceServer(OAuth2ResourceServerConfigurer::jwt);
        return http.build();
    }
}

For applications that accept tokens via query parameters or custom headers (not recommended), apply the same length and pattern checks. Avoid logging raw token values; if logging is necessary for debugging, mask sensitive portions. middleBrick’s LLM/AI Security checks can verify that your implementation does not inadvertently expose tokens in prompts or tool calls, especially when integrating with AI coding assistants via MCP Server.

Finally, use dependency checks to ensure that any libraries processing tokens are up to date and not vulnerable to known buffer issues. Combine these code-level fixes with continuous scanning using the middleBrick CLI to validate that your security posture remains strong across changes. The GitHub Action can enforce a maximum risk score threshold, failing builds if new findings introduce buffer-related weaknesses.