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Introduction:
The cybersecurity industry has long grappled with a painful paradox: the most effective vulnerability detection tools are often too expensive, too slow, or too risky to deploy at scale. Running proprietary source code through cloud-based frontier models introduces compliance nightmares, while manual code reviews across million-line repositories are simply unsustainable. Cisco’s Foundation AI team has now unveiled Antares — a family of open-weight small language models (SLMs) specifically engineered for repository-scale vulnerability localization. With 350 million and 1 billion parameter versions already available on Hugging Face, Antares promises to localize known vulnerabilities at a fraction of the cost of frontier systems, all while keeping sensitive code entirely within your own infrastructure.
Learning Objectives:
- Understand how Antares leverages evidence-driven exploration to pinpoint vulnerable files in large codebases using only CWE descriptions.
- Learn to deploy and run Antares-350M and Antares-1B locally via command-line interface, including repository-wide scans and SARIF output integration.
- Master the practical workflow of combining Antares with existing security toolchains for continuous vulnerability triage and remediation.
- What Is Antares and Why Does It Matter?
Antares is not another chatbot or general-purpose LLM. It is a specialized vulnerability localization agent that operates like a human investigator: given a Common Weakness Enumeration (CWE) identifier and description, it searches the codebase, reads candidate files, backtracks when paths lead nowhere, and returns a ranked list of files most likely to contain the weakness. The model family currently includes Antares-350M (with a 32,000-token context window) for resource-constrained environments and Antares-1B (128,000-token window) designed for single-GPU deployments. A larger 3-billion-parameter version has also been announced.
What makes Antares revolutionary is its cost-performance ratio. According to Cisco’s chief AI scientist Amin Karbasi, Antares can scan 500 code repositories in approximately 15 minutes at a cost of less than $1, compared to five hours and $100–$150 for frontier models. On Cisco’s internal VLoc benchmark — comprising 500 tasks, 290 repositories, six software ecosystems, and 147 CWE categories — Antares-1B achieved a File F1 score of 0.209, with the unreleased Antares-3B reaching 0.223, approaching the performance of GPT-5.5.
Step-by-Step: Getting Started with Antares
- Access the Models: Visit Cisco’s official Hugging Face collection at https://huggingface.co/collections/fdtn-ai/antares. Note that access is approved case by case.
- Review the Model Card: Examine the documentation for Antares-1B (https://huggingface.co/fdtn-ai/antares-1b) and Antares-350M (https://huggingface.co/fdtn-ai/antares-350m).
- Set Up Local Inference: Deploy the model on a machine with sufficient resources — a single GPU for Antares-1B or constrained environments for Antares-350M.
- Run a Targeted CWE Investigation: Use the CLI to investigate a specific CWE across your repository.
- Output Formats: The tool returns candidate files in human-readable, JSON, or SARIF format for integration with existing review workflows.
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Local Deployment: Keeping Your Code Where It Belongs
Perhaps the most compelling feature of Antares is its ability to run entirely offline. For organizations in regulated industries — public sector, healthcare, finance, and defense — sending proprietary source code to cloud-based AI providers is often a non-starter. Antares addresses this by running locally, ensuring that code never leaves your environment. This local-first approach not only satisfies compliance requirements but also eliminates the recurring costs associated with API-based cloud inference.
Step-by-Step: Local Deployment and Security Hardening
- Isolate the Environment: Deploy Antares in a dedicated, air-gapped or network-isolated analysis environment.
- Verify Model Integrity: Check model files and inference dependencies to mitigate AI model supply-chain risks.
- Limit Access: Restrict administrative access and implement robust logging for all analysis activities.
- Run Repository-Wide Scans: Execute scans against a read-only copy of your repository to prevent accidental modifications.
- Integrate Output: Use SARIF output to feed candidate files directly into your existing security review workflows.
Linux Command Example for Repository Analysis:
Clone a read-only copy of the repository git clone --mirror https://github.com/example/repo.git /tmp/repo-mirror Run Antares CLI for a specific CWE (example: CWE-89 for SQL Injection) antares scan --cwe CWE-89 --repo /tmp/repo-mirror --output sarif > results.sarif Parse the SARIF output for candidate files cat results.sarif | jq '.runs[].results[].locations[].physicalLocation.artifactLocation.uri'
Windows PowerShell Equivalent:
Clone repository mirror git clone --mirror https://github.com/example/repo.git C:\temp\repo-mirror Run Antares scan antares scan --cwe CWE-89 --repo C:\temp\repo-mirror --output json > results.json Extract candidate files Get-Content results.json | ConvertFrom-Json | Select-Object -ExpandProperty candidates
3. The Evidence-Driven Exploration Agent Paradigm
Unlike traditional static analysis tools such as Semgrep or CodeQL, which rely on predefined rules or queries, Antares operates as an evidence-driven exploration agent. It adapts its search strategy dynamically as it traverses the repository, using terminal commands like grep, find, and `cat` to inspect files and gather evidence. This approach allows Antares to uncover vulnerabilities that might evade rule-based scanners, particularly in large, heterogeneous codebases.
Step-by-Step: Understanding the Exploration Workflow
- Input: The model receives a CWE identifier and a generic description of the vulnerability class.
- Search Initiation: Antares begins by issuing exploratory commands to understand the repository structure.
- Evidence Collection: It reads candidate files, extracts relevant code patterns, and evaluates them against the vulnerability description.
- Backtracking: When a search path yields no results, the model backtracks and explores alternative routes.
- Ranking: The model returns a ranked list of source files most likely to contain the vulnerability, along with the terminal exploration trace that led to each result.
Key Commands Used by Antares Internally:
Find all files containing a specific pattern grep -r "unsafe" --include=".c" /path/to/repo Locate files with specific extensions find /path/to/repo -1ame ".js" -o -1ame ".ts" Read file contents for analysis cat /path/to/repo/src/main.c
4. Benchmarking and Performance: Small Model, Big Results
Cisco’s internal VLoc benchmark is designed to evaluate how well models can identify implementation files later changed by a security patch. The results are striking: Antares-1B outperforms Google’s Gemini 3 Pro and matches Z.ai’s GLM-5.2, while the unreleased Antares-3B tops both GLM-5.2 and OpenAI’s GPT-5.5. This performance is achieved with models that are orders of magnitude smaller and cheaper to run than their frontier counterparts.
Step-by-Step: Evaluating Antares on Your Own Codebase
- Select a Benchmark Repository: Choose a repository with known CVEs and corresponding patches.
- Run Antares Scan: Execute a scan using the CWE associated with each known vulnerability.
- Compare Results: Evaluate the File F1 score — the proportion of correctly identified vulnerable files.
- Analyze False Positives: Review the ranked list to understand where the model may have misidentified files.
- Iterate: Use the terminal exploration trace to refine your understanding of the model’s decision-making process.
5. Integration with Existing Security Toolchains
Antares is explicitly designed not to replace the broader application security toolchain but to augment it. Human analysts or downstream security tools are still needed to confirm exploitability, identify vulnerable lines of code, assess severity, and generate fixes. This makes Antares an ideal complement to existing SAST, DAST, and manual review processes.
Step-by-Step: Building a Continuous Vulnerability Operation
- Automated Scanning: Schedule regular Antares scans across your entire repository estate.
- Triage: Use the ranked list of candidate files to prioritize human review efforts.
- Remediation: Integrate with issue tracking systems (e.g., Jira, GitHub Issues) to assign remediation tasks.
- Verification: After patches are applied, re-run Antares to confirm that vulnerabilities have been addressed.
- Continuous Improvement: Use the terminal exploration traces to train your security team on emerging vulnerability patterns.
Example CI/CD Integration (GitHub Actions):
name: Antares Vulnerability Scan on: push: branches: [ main ] jobs: security-scan: runs-on: ubuntu-latest steps: - uses: actions/checkout@v3 - name: Run Antares Scan run: | antares scan --cwe CWE-89 --repo . --output sarif > results.sarif - name: Upload SARIF Results uses: github/codeql-action/upload-sarif@v2 with: sarif_file: results.sarif
- The Dual-Use Dilemma: Why Cisco Is Gating Access
Cisco is not releasing Antares without safeguards. A tool that finds vulnerabilities is also a tool that helps attackers exploit them. As Reza Shokri, a computer scientist at the National University of Singapore, noted, AI agents now “write more of the code, and are growing capable of exploiting it”. Cisco is therefore approving access to Antares case by case and holding back the larger 3-billion-parameter model from full public release. This responsible approach acknowledges the dual-use nature of offensive security tools.
Step-by-Step: Responsible Use and Risk Mitigation
- Access Request: Apply for access through Cisco’s Hugging Face collection with appropriate justification.
- Use Isolation: Deploy Antares in a controlled, monitored environment separate from production systems.
- Implement Guardrails: Restrict model capabilities to read-only repository analysis.
- Monitor Usage: Log all analysis activities and review them regularly for anomalies.
- Stay Updated: Follow Cisco’s announcements for updates on model versions and security patches.
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Future Directions: Antares and the Democratization of AI Security
Antares represents a fundamental shift in how AI can be applied to cybersecurity. By demonstrating that small, specialized models can outperform massive frontier systems on specific tasks, Cisco is challenging the prevailing “bigger is better” narrative in AI. This democratization of AI security means that even small teams with limited budgets can now afford continuous, AI-driven vulnerability localization.
Cisco has also introduced complementary initiatives: Foundry Security Spec (defining agentic security evaluation blueprints) and CodeGuard (embedding “secure by default” rules into AI programming agents). Together with Antares, these tools form a coherent ecosystem for responsible AI security.
Step-by-Step: Building a Future-Ready AI Security Program
- Adopt Antares: Begin with Antares-350M or Antares-1B for vulnerability localization.
- Implement Foundry Security Spec: Use it to define security evaluation criteria for AI agents.
- Deploy CodeGuard: Integrate secure coding rules into your AI-assisted development workflows.
- Continuous Scanning: Transition from point-in-time assessments to ongoing vulnerability operations.
- Measure and Improve: Track metrics like Mean Time to Detect (MTTD) and Mean Time to Remediate (MTTR) to quantify improvements.
What Undercode Say:
- Key Takeaway 1: Antares proves that specialized, task-trained small language models can outperform general-purpose frontier models in specific security tasks, challenging the industry’s obsession with scale.
- Key Takeaway 2: Local deployment is not just a compliance feature — it’s a strategic advantage that eliminates recurring cloud inference costs and keeps sensitive code within your trust boundary.
Analysis: Cisco’s Antares is a watershed moment for AI-powered cybersecurity. By open-sourcing these models (with gated access), Cisco is effectively lowering the barrier to entry for AI-driven vulnerability localization. The cost-performance ratio — scanning 500 repositories in 15 minutes for under $1 — makes continuous security scanning feasible for organizations of all sizes. However, the dual-use nature of such tools cannot be overstated: the same capabilities that help defenders find bugs can also be weaponized by attackers. Cisco’s decision to gate access and withhold the 3B model is prudent, but it also raises questions about equitable access to advanced security AI. The broader implication is that we are entering an era where AI agents will increasingly handle the grunt work of security analysis, allowing human experts to focus on high-value tasks like exploit validation and remediation strategy. The challenge will be ensuring that these tools are deployed responsibly and that their use does not inadvertently create new attack surfaces.
Prediction:
- +1 Antares will accelerate the adoption of continuous vulnerability operations, shifting security teams from periodic assessments to real-time, AI-driven monitoring.
- +1 The success of Antares will inspire a wave of specialized, open-weight security models targeting other niches — such as cloud misconfiguration detection, API security, and IAM policy analysis.
- -1 The gated access model may create a two-tier security landscape where well-resourced organizations gain early access to advanced AI defenses, while smaller teams lag behind.
- -1 As attackers inevitably adopt similar AI agents for vulnerability discovery, organizations will face an escalating arms race, requiring faster patch cycles and more sophisticated defense-in-depth strategies.
- +1 Cisco’s ecosystem approach — combining Antares with Foundry Security Spec and CodeGuard — will establish a new benchmark for responsible AI security, influencing how other vendors design and release their own AI models.
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