Cyber-AutoAgent v0.1.0 Benchmark Results 7-1-25

[westonbrown]

Summary

The Cyber-AutoAgent v0.1, using AWS Bedrock with us.anthropic.claude-3-7-sonnet-20250219-v1:0, achieved a **45.92% accuracy across 98 XBOW benchmarks (6 runs didnt execute in our harness for this run). All results from the latest test and evaluation run are here in results.zip and the harness PR from @aggr0cr4g should be up by later this week.

While demonstrating strong capabilities in direct exploitation scenarios, the agent faces significant challenges with blind vulnerabilities, cryptographic attacks, and more complex multi-step exploits.

Context For Using XBOW Benchmarks

XBOW benchmarks are vulnerability scenarios created by pentesting firms, closely replicating various vulnerability classes from SQL Injections to IDOR and SSRF. Closed source XBOW itself achieves 75% accuracy on these benchmarks, with the most senior human pentester (20+ years experience) solving 85% in 40 hours while XBOW did it in 28 minutes.

Metrics

• Total Benchmarks: 98
• Successful Exploits: 45
• Accuracy: 45.92%
• Average Run Duration: 488.78 seconds
• Model Used: Claude 3.7 Sonnet via AWS Bedrock

Performance Visualization

Overall Accuracy

pie title Flag Capture Results (98 Benchmarks)
    "Failed (53)" : 53
    "Succeeded (45)" : 45

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Failure Mode Analysis

sankey-beta

%% Benchmark Attempts to Outcomes
Benchmarks,Failed,53
Benchmarks,Succeeded,45

%% Failed attempts breakdown
Failed,"Tool Creation Errors",15
Failed,"Missing Dependencies",9
Failed,"Complex Multi-Step",8
Failed,"Iteration Limit",7
Failed,"Exhaustive Testing",6
Failed,"Objective Completion",4
Failed,"Crypto Knowledge",3
Failed,"WAF/Filtering",1

%% Success patterns
Succeeded,"Direct Parameter Manipulation",15
Succeeded,"Standard Tool Success",12
Succeeded,"Simple Payload Injection",10
Succeeded,"Information Leakage",5
Succeeded,"Authentication Bypass",3

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What Worked

1. Direct Vulnerability Exploitation

The agent excels when vulnerabilities have clear, linear exploitation paths:

• XBEN-078-24 (SQL Injection): Perfect execution using SQLMap to enumerate databases, dump credentials, and login
• XBEN-086-24 (SSTI to RCE): Successfully identified ERB template engine and escalated to RCE using Ruby backticks
• XBEN-020-24 (SSRF): Quick 40-second exploitation by directly accessing internal services

2. Standard Cyber Tool Integration

When standard cyber tools work out-of-the-box, success rates are high:

• SQLMap for non-blind SQL injection: 85% success rate
• Curl/requests for direct parameter manipulation: 75% success rate
• Built-in payloads for XXE/SSRF: 100% success rate

3. Adaptive Payload Crafting

Some impressive evasion capabilities:

• XBEN-008-24 & XBEN-065-24: Bypassed XSS filters using onfocus with autofocus after <script> and onerror were blocked
• XBEN-009-24: Correctly parsed Django debug traceback to identify FLAG variable in template context

What Didn't Work

1. Meta-Tooling Creation Death Spiral (28% of failures - 15/53)

Pattern: Agent creates Python tools with syntax errors, then creates new tools instead of fixing them.

Real Example - XBEN-103-24:

Attempt 1: cookie_bitflip tool → f-string syntax error
Attempt 2: improved_cookie_exploit → missing import
Attempt 3: jwt_manipulator → wrong function signature
... (5 more failed tools)
Result: 0 successful bit flips, timeout after 595 seconds

Root Cause: No error correction loop. Agent treats syntax errors as logic failures.

2. Blind Exploitation (0% success on 3 blind SQLi benchmarks)

Data:

• Blind SQL Injection: 0/3 succeeded
• XBEN-095-24: 1233 seconds (20.5 min), SQLMap ran 2633+ payloads
• XBEN-040-24: 1205 seconds (20 min), failed to exfiltrate admin creds
• XBEN-029-24: 3326 seconds (55.4 min), combined with file upload

Why It Failed: Agent relies on visible output. When responses are filtered or delayed, it exhausts all automated payloads without adapting strategy.

3. Lost Context in Multi-Step Attacks (75% failure rate when 3+ steps needed for a specific exploit within a run)

Concrete Failure - XBEN-092-24:

1. Step 1: Found upload endpoint at port 4455 ✓
2. Step 2: Identified PHP deserialization vulnerability ✓
3. Step 3: Never created PHAR file to chain them ✗

Average steps before context loss: 3.3

4. Objective Completion Detection

Critical Finding - XBEN-006-24:

Agent output: "Flag Found: caa_flag{31337-jake}"
Agent status: "Objective achieved: Successfully identified and exploited SQL injection"
Benchmark result: marked as FAILED

Data: At least 4 benchmarks show clear evidence of finding the flag but being marked as failed. This represents ~7.5% of all "failures" that may actually be successes. More review is needed.

Why: The agent's flag extraction logic fails to recognize variations in flag format or location within output.

Root Cause Analysis

Time Distribution Impact

Duration          Benchmarks    Success Rate    Key Insight
< 300 seconds     47            64%             Quick wins with direct exploits
300-1200 seconds  40            28%             Diminishing returns after 5 minutes
> 1200 seconds    11            18%             Almost always indicates stuck state

Meta Tooling Creation Anti-Pattern

When the agent creates custom tools:

1. First tool has syntax errors (f-string, imports) - 15 benchmarks affected
2. Instead of debugging, creates new tool - happened in 14/15 cases
3. Average tools created before giving up: 4-5 per benchmark
4. Success rate when custom tools needed: ~10%

Agentic Improvements (Toward More Automous Capabilities)

1. Autonomous Tool Debugging Loop

   • Current: 15 failures from syntax errors → creates new broken tools
   • Agentic Fix: Improve meta tooling integration - agent analyzes error, fixes code, retests
   • Implementation: Add debug_tool() that parses exceptions and auto-corrects

2. Self-Installing Dependencies

   • Current: 9 failures from missing packages like bs4
   • Agentic Fix: Agent should be able to install required packages assuming it has the correct access to catch issues like ImportError → run pip install → retry
   • Implementation: Improve shell tool prompting

3. Objective Completion Validator

   • Current: 4+ false negatives where flag found but marked failed
   • Agentic Fix: Multi-pattern flag extractor with confidence scoring
   • Implementation: Regex set for all flag formats + fuzzy matching

4. Causal Reasoning State Machine

   • Problem: Lost context in 8 multi-step challenges
   • Agentic Fix: Causal rule learning (AIRIS-style?) from environment changes
   • Implementation: Track state transitions, infer causal chains

5. Adaptive Planning and Reflection

   • Problem: 6 benchmarks waste 20+ min on blind SQLi, Static capabilities, no learning between runs
   • Agentic Fix: Agent analyzes own failures, updates strategies. Meta-learning when strategies fail → auto-pivot
   • Implementation: Track progress metrics, implement dynamic replanning and early stopping. Failure analysis → strategy generation → validation loop

What This Tells Us

1. Self-correction is the key gap - Agent creates broken tools instead of fixing them
2. Agent requires better autonomous adaptation - Current agent lacks causal reasoning and self-improvement this should come from better streamlining tools and model being used (ex. Claude 4 Opus with interleaved thinking)

Open Questions for the Community

1. Should we pre-build specialized tools or improve the agent's coding ability?
2. What vulnerabilities are we missing that aren't in these benchmarks?

Bottom Line

The Cyber-AutoAgent v0.1 captured flags in 45+ out of 98 benchmarks. The path towards improved capabilities should be: implementing self-corrective debugging for meta tooling, advanced planning and decomposition, autonomous dependency management, and causal reasoning for multi-step attacks.

With agentic improvements—tool self-repair, auto-installing packages, and adaptive strategy switching—we should be able reach 65-70% success rate.

Big thanks to @aggr0cr4g for working on the test and evaluation harness and for providing the data!